Libraries

# Import Libraries 
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import plotly.express as px
import pandas as pd
!pip install geopandas
from matplotlib.ticker import FormatStrFormatter
import geopandas
from geopandas import GeoSeries
from shapely.geometry import Polygon
from sklearn.metrics import mean_absolute_error
from sklearn import metrics
from imblearn.over_sampling import SMOTE
from sklearn.linear_model import LogisticRegression
from matplotlib import pyplot
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn import tree
from sklearn.preprocessing import scale, normalize
!pip install dtreeviz
from dtreeviz.trees import dtreeviz
from sklearn import preprocessing
import seaborn as sns
from sklearn.utils import shuffle
from sklearn.model_selection import StratifiedShuffleSplit
import plotly.express as px
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.mixture import GaussianMixture
from scipy import stats
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import numpy
from sklearn.tree import export_graphviz
import pydot
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
import seaborn as sns
from shapely.geos import lgeos
import requests 
from bs4 import BeautifulSoup
import nltk
from collections import Counter
#import fiona
from shapely.geometry import Polygon
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import seaborn as sns; sns.set()
import csv
from sklearn.metrics import accuracy_score
import datetime
import urllib.request
 
from bs4 import BeautifulSoup
 
from math import radians, cos, sin, asin, sqrt
!pip install pygeos
!pip install rtree
!pip install geopandas
!pip install git+git://github.com/geopandas/geopandas.git
 
import geopandas
import pygeos
import rtree 
 
!pip install metpy
from metpy.interpolate import (interpolate_to_grid, remove_nan_observations,
                               remove_repeat_coordinates)
 
# Import necessary packages
from bs4 import BeautifulSoup
import requests
import pandas as pd
import re
%matplotlib inline
import folium
 

Web scrapping website: https://towardsdatascience.com/web-scraping-scraping-table-data-1665b6b2271c

1.Colville River

USGS: COLVILLE R AT UMIAT AK

Variables:

Monthly Mean Water Temperature = 'USGS_Colville_temperature'

Latitude 69°21'37.8", Longitude 152°07'21.7" NAD83
Drainage area 13,860 square miles
Gage datum 267.2 feet above NAVD88

Water Quality Samples

variable = 'USGS_Colville_water'

# Getting the HTML 
url = 'https://nwis.waterdata.usgs.gov/ak/nwis/qwdata/?site_no=15875000&agency_cd=USGS&inventory_output=0&rdb_inventory_output=value&TZoutput=0&pm_cd_compare=Greater%20than&radio_parm_cds=all_parm_cds&format=html_table&qw_attributes=0&qw_sample_wide=wide&rdb_qw_attributes=0&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=brief_list'
 
# Make a GET request to fetch the raw HTML content
html_content = requests.get(url).text
 
# Parse HTML code for the entire site
soup = BeautifulSoup(html_content, "lxml")
 
#print(soup.prettify()) # print the parsed data of html

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Number of tables on site:  2

# Lets go ahead and scrape first table with HTML code gdp[0]
table1 = gdp[0]
 
# the head will form our column names
body = table1.find_all("tr")
 
# Head values (Column names) are the first items of the body list
head = body[0] # 0th item is the header row
body_rows = body[1:] # All other items becomes the rest of the rows
 
# Lets now iterate through the head HTML code and make list of clean headings
 
# Declare empty list to keep Columns names
headings = []
for item in head.find_all("th"): # loop through all th elements
    # convert the th elements to text and strip "\n"
    item = (item.text).rstrip("\n")
    # append the clean column name to headings
    headings.append(item)
    
print(headings)

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['SampleDatetime', 'Timedatum\xa0', 'Timedatumreliabilitycode\xa0', 'SampleMediumCode\xa0', 'AgencyCollectingSample,Code\xa0', '     Stream width,  feet(00004)', ' Loca-tion inX-sect.lookingdwnstrmft from l bank(00009)', '   Temper- ature, water, deg C(00010)', '   Temper- ature,  air, deg C(00020)', '   Baro- metric pres- sure, mm Hg(00025)', '  Instan-taneous  dis-charge, ft3/s(00061)', '      Gageheight,  feet(00065)', '    Turbid-  ity,  JTU(00070)', '   Color, water, fltrd, Pt-Co units(00080)', 'Specif-   icconduc- tance,wat unfuS/cm @25 degC(00095)', ' Hydro-  gen  ion, water,unfltrd calcd,  mg/L(00191)', '     Dis- solvedoxygen,  mg/L(00300)', '   Dis- solvedoxygen,percentof sat-uration(00301)', '   pH, water,unfltrd field,  std units(00400)', '   Carbondioxide water,unfltrd  mg/L(00405)', '  ANC,wat unf fixedend pt, field,mg/L as CaCO3(00410)', ' Bicar-bonate,wat unf fixedend pt, field,  mg/L(00440)', 'Carbon-  ate,wat unf fixedend pt, field,  mg/L(00445)', '  Nitrate water, fltrd,  mg/L  as N(00618)', 'Nitrate   +nitrite water, fltrd,  mg/L  as N(00631)', '  Ortho- phos- phate, water, fltrd,  mg/L(00660)', ' Ortho- phos- phate, water, fltrd,  mg/L  as P(00671)', '  Organiccarbon, water,unfltrd  mg/L(00680)', '   Hard- ness, water,mg/L as CaCO3(00900)', 'Noncarb hard- ness,wat unf field,mg/L as CaCO3(00902)', '   Calcium water, fltrd,  mg/L(00915)', '  Magnes-  ium, water, fltrd,  mg/L(00925)', '   Sodium, water, fltrd,  mg/L(00930)', '    Sodiumadsorp-  tion ratio(00931)', '  Sodium frac-  tion   ofcationspercent(00932)', '   Potas- sium, water, fltrd,  mg/L(00935)', '   Chlor-  ide, water, fltrd,  mg/L(00940)', '   Sulfate water, fltrd,  mg/L(00945)', '   Fluor-  ide, water, fltrd,  mg/L(00950)', '  Silica, water, fltrd,mg/L as  SiO2(00955)', '    Iron, water, fltrd,  ug/L(01046)', '   Manga- nese, water, fltrd,  ug/L(01056)', '    Gage height above datum meters(30207)', '    Dis-charge,instan-taneous  m3/s(30209)', ' Fecal coli- form,  M-FC0.7u MF  cfu/ 100 mL(31625)', ' Fecal strep-tococci   KF  MF,  cfu/ 100 mL(31673)', '  Dis- solved solidsdried @180degCwat flt  mg/L(70300)', '  Dis- solvedsolids,sum  ofconsti-tuents,  mg/L(70301)', '    Dis- solvedsolids, water, ton/d(70302)', '   Dis- solvedsolids, water, tons/acre-ft(70303)', '   Nitrate water, fltrd,  mg/L(71851)', '    Iron, water,unfltrd  ug/L(71885)', '     Samplepurpose  code(71999)', '     Sam- plingmethod,  code(82398)']

# Next loop though the rest of the rows
 
#print(body_rows[0])
all_rows = [] # will be a list for list for all rows
for row_num in range(len(body_rows)): # A row at a time
    row = [] # this will old entries for one row
    for row_item in body_rows[row_num].find_all("td"): #loop through all row entries
        # row_item.text removes the tags from the entries
        # the following regex is to remove \xa0 and \n and comma from row_item.text
        # xa0 encodes the flag, \n is the newline and comma separates thousands in numbers
        aa = re.sub("(\xa0)|(\n)|,","",row_item.text)
        #append aa to row - note one row entry is being appended
        row.append(aa)
    # append one row to all_rows
    all_rows.append(row)

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headings = ['SampleDatetime',
 'Time datum',
 'Time datum reliability code',
 'Sample Medium Code',
 'Agency Collecting Sample,Code',
 'Stream width(ft)',
 'Location intersect (lookingdwnstream) from l bank)',
 'Temperature,water(C)(00010)',
 'Temperature,air(C)(00020)',
 'Barometric pressure,(mmHg)(00025)',
 'Instantaneous_discharge(ft3/s)(00061)',
 'Gageheight_feet(00065)',
 'Turbidity,(JTU)(00070)',
 'Color, water, fltrd, Pt-Co units(00080)',
 'Specific_conductance,wat unfuS/cm @25 degC(00095)',
 'Hydrogen_ion,water,unfltrd calcd,mg/L(00191)',
 'Dissolved_oxygen,(mg/L)(00300)',
 'Dissolved_oxygen,percent of saturation(00301)',
 'pH, water,unfltrd field,  std units(00400)',
 'Carbon_dioxide water,unfltrd  mg/L(00405)',
 'ANC,wat unffixedend pt, field,mg/L as CaCO3(00410)',
 'Bicarbonate,wat unf fixedend pt, field,  mg/L(00440)',
 'Carbonate,wat unf fixedend pt, field,  mg/L(00445)',
 'Nitrate water, fltrd,  mg/L  as N(00618)',
 'Nitrate+nitrite water, fltrd, mg/L  as N(00631)',
 'Ortho-phosphate, water, fltrd, mg/L(00660)',
 'Ortho-phosphate, water, fltrd, mg/L  as P(00671)',
 'Organic_carbon, water,unfltrd  mg/L(00680)',
 'Hardness, water,mg/L as CaCO3(00900)',
 'Noncarb hardness,wat unf field,mg/L as CaCO3(00902)',
 'Calcium water, fltrd,  mg/L(00915)',
 'Magnesium, water, fltrd,  mg/L(00925)',
 'Sodium, water, fltrd,  mg/L(00930)',
 'Sodium_adsorption ratio(00931)',
 'Sodium fraction of cations percent(00932)',
 'Potassium, water, fltrd,  mg/L(00935)',
 'Chloride, water, fltrd,  mg/L(00940)',
 'Sulfate water, fltrd,  mg/L(00945)',
 'Fluoride, water, fltrd,  mg/L(00950)',
 'Silica, water, fltrd,mg/L as  SiO2(00955)',
 'Iron, water, fltrd,  ug/L(01046)',
 'Manga- nese, water, fltrd, ug/L(01056)',
 'Gage_height_meters(30207)',
 'Discharge_instantaneous(m3/s)(30209)',
 'Fecal coli-form,M-FC0.7u MF cfu/ 100 mL(31625)',
 'Fecal streptococci KF MF, cfu/ 100 mL(31673)',
 'Dissolved_solids_dried @180degCwat flt mg/L(70300)',
 'Dissolved_solids, sum of constituents, mg/L(70301)',
 'Dissolved_solids, water, ton/d(70302)',
 'Dissolved_solids, water, tons/acre-ft(70303)',
 'Nitrate water, fltrd,  mg/L(71851)',
 'Iron, water,unfltrd  ug/L(71885)',
 'Sample purpose code(71999)',
 'Sampling method code(82398)']

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Index(['SampleDatetime', 'Time datum', 'Time datum reliability code',
       'Sample Medium Code', 'Agency Collecting Sample,Code',
       'Stream width(ft)',
       'Location intersect (lookingdwnstream) from l bank)',
       'Temperature,water(C)(00010)', 'Temperature,air(C)(00020)',
       'Barometric pressure,(mmHg)(00025)',
       'Instantaneous_discharge(ft3/s)(00061)', 'Gageheight_feet(00065)',
       'Turbidity,(JTU)(00070)', 'Color, water, fltrd, Pt-Co units(00080)',
       'Specific_conductance,wat unfuS/cm @25 degC(00095)',
       'Hydrogen_ion,water,unfltrd calcd,mg/L(00191)',
       'Dissolved_oxygen,(mg/L)(00300)',
       'Dissolved_oxygen,percent of saturation(00301)',
       'pH, water,unfltrd field,  std units(00400)',
       'Carbon_dioxide water,unfltrd  mg/L(00405)',
       'ANC,wat unffixedend pt, field,mg/L as CaCO3(00410)',
       'Bicarbonate,wat unf fixedend pt, field,  mg/L(00440)',
       'Carbonate,wat unf fixedend pt, field,  mg/L(00445)',
       'Nitrate water, fltrd,  mg/L  as N(00618)',
       'Nitrate+nitrite water, fltrd, mg/L  as N(00631)',
       'Ortho-phosphate, water, fltrd, mg/L(00660)',
       'Ortho-phosphate, water, fltrd, mg/L  as P(00671)',
       'Organic_carbon, water,unfltrd  mg/L(00680)',
       'Hardness, water,mg/L as CaCO3(00900)',
       'Noncarb hardness,wat unf field,mg/L as CaCO3(00902)',
       'Calcium water, fltrd,  mg/L(00915)',
       'Magnesium, water, fltrd,  mg/L(00925)',
       'Sodium, water, fltrd,  mg/L(00930)', 'Sodium_adsorption ratio(00931)',
       'Sodium fraction of cations percent(00932)',
       'Potassium, water, fltrd,  mg/L(00935)',
       'Chloride, water, fltrd,  mg/L(00940)',
       'Sulfate water, fltrd,  mg/L(00945)',
       'Fluoride, water, fltrd,  mg/L(00950)',
       'Silica, water, fltrd,mg/L as  SiO2(00955)',
       'Iron, water, fltrd,  ug/L(01046)',
       'Manga- nese, water, fltrd, ug/L(01056)', 'Gage_height_meters(30207)',
       'Discharge_instantaneous(m3/s)(30209)',
       'Fecal coli-form,M-FC0.7u MF cfu/ 100 mL(31625)',
       'Fecal streptococci KF MF, cfu/ 100 mL(31673)',
       'Dissolved_solids_dried @180degCwat flt mg/L(70300)',
       'Dissolved_solids, sum of constituents, mg/L(70301)',
       'Dissolved_solids, water, ton/d(70302)',
       'Dissolved_solids, water, tons/acre-ft(70303)',
       'Nitrate water, fltrd,  mg/L(71851)',
       'Iron, water,unfltrd  ug/L(71885)', 'Sample purpose code(71999)',
       'Sampling method code(82398)'],
      dtype='object')

# Adding to Other DataFrames
 
# Adding this Dataframe into the big Colville Dataframe
Colville = pd.DataFrame(USGS_Colville_water,columns=['SampleDatetime',
 'Stream width(ft)',
 'Temperature,water(C)(00010)',
 'Temperature,air(C)(00020)',
 'Instantaneous_discharge(ft3/s)(00061)',
 'Gageheight_feet(00065)',
 'Color, water, fltrd, Pt-Co units(00080)',
 'Specific_conductance,wat unfuS/cm @25 degC(00095)',
 'Hydrogen_ion,water,unfltrd calcd,mg/L(00191)',
 'Dissolved_oxygen,(mg/L)(00300)',
 'Dis- solvedoxygen,percentof sat-uration(00301)',
 'pH, water,unfltrd field,  std units(00400)',
 'Carbondioxide water,unfltrd  mg/L(00405)',
 'Bicarbonate,wat unf fixedend pt, field,  mg/L(00440)',
 'Carbonate,wat unf fixedend pt, field,  mg/L(00445)',
 'Nitrate water, fltrd,  mg/L  as N(00618)',
 'Nitrate+nitrite water, fltrd, mg/L  as N(00631)',
 'Ortho-phosphate, water, fltrd, mg/L(00660)',
 'Ortho-phosphate, water, fltrd, mg/L  as P(00671)',
 'Organic_carbon, water,unfltrd  mg/L(00680)',
 'Hardness, water,mg/L as CaCO3(00900)',
 'Noncarb hardness,wat unf field,mg/L as CaCO3(00902)',
 'Calcium water, fltrd,  mg/L(00915)',
 'Magnesium, water, fltrd,  mg/L(00925)',
 'Sodium, water, fltrd,  mg/L(00930)',
 'Sodium_adsorption ratio(00931)',
 'Sodium fraction of cations percent(00932)',
 'Potassium, water, fltrd,  mg/L(00935)',
 'Chloride, water, fltrd,  mg/L(00940)',
 'Sulfate water, fltrd,  mg/L(00945)',
 'Fluoride, water, fltrd,  mg/L(00950)',
 'Silica, water, fltrd,mg/L as  SiO2(00955)',
 'Iron, water, fltrd,  ug/L(01046)',
 'Manga- nese, water, fltrd, ug/L(01056)',
 'Gage_height_meters(30207)',
 'Discharge_instantaneous(m3/s)(30209)',
 'Fecal coli-form,M-FC0.7u MF cfu/ 100 mL(31625)',
 'Fecal streptococci KF MF, cfu/ 100 mL(31673)',
 'Dissolved_solids_dried @180degCwat flt mg/L(70300)',
 'Dissolved_solids, sum of constituents, mg/L(70301)',
 'Dissolved_solids, water, ton/d(70302)',
 'Dissolved_solids, water, tons/acre-ft(70303)',
 'Nitrate water, fltrd,  mg/L(71851)',
 'Iron, water,unfltrd  ug/L(71885)'])

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Mean Daily Discharge (ft3/s)

variable = 'USGS_Colville_discharge'
Calculation Period 2001-10-01 -> 2021-09-30

website: https://waterdata.usgs.gov/nwis/dvstat/?referred_module=sw&site_no=15875000&por_15875000_1200=624322,00060,1200,2002-08-21,2021-04-19&format=html_table&stat_cds=mean_va&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=parameter_selection_list

USGS_Colville_discharge = pd.DataFrame({'Day':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],
                   'Jan':[40,39,36,35,34,34,32,30,29,28,27,26,25,24,24,23,22,21,20,19,19,18,18,17,17,16,15,15,14,14,13],
                   'Feb':[13,13,12,12,11,11,11,11,10,10,9,9,9,9,9,8,8,8,8,7,7,7,7,6,6,6,6,6,4,'NaN','NaN'],
                   'Mar':[6,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3],
                   'Apr':[3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3.1,3.1,3.2,3.2,3.3,3.6,6.3,'NaN'],
                   'May':[14,37,114,203,142,126,115,104,99,105,94,95,100,2030,4470,3890,6880,13400,18900,22500,28100,34500,42900,54600,59700,69500,70900,69300,75200,89300,93600],
                   'Jun':[89100,82200,77200,75700,76500,73400,73100,66900,59900,56600,54700,49600,42600,37000,33000,30900,28900,26800,25200,24700,23800,23900,23500,25300,22900,21200,21500,22700,22000,19100,'NaN'],
                   'Jul':[17500,17700,17400,20200,17500,15300,14000,12700,12100,12900,15200,15900,14800,13800,13400,14500,13500,13500,14700,17100,19400,18900,18300,19200,18100,16000,15800,15600,14500,13600,13300],
                   'Aug':[15700,19700,19200,19700,23000,21800,19600,19300,18500,17100,16200,16100,17500,22200,26300,24400,21900,23700,26400,23800,21200,20500,21200,22100,21800,21900,20800,21800,21100,19900,19600],
                   'Sep':[19800,21200,22200,22100,19800,18100,17300,17300,16500,16300,16300,15100,14600,13900,14100,14400,14100,13300,12700,12200,11700,11300,11300,10700,9840,9160,8370,7780,7150,6750,'NaN'],
                   'Oct':[6430,6360,7130,9900,8980,6970,5990,5190,4530,3970,3570,3210,2950,2680,2280,2030,1800,1610,1470,1380,1290,1210,1120,1060,988,941,891,841,794,752,707],
                   'Nov':[674,639,607,572,546,514,496,471,445,419,402,376,361,343,324,309,297,274,266,252,244,226,219,206,199,191,182,174,168,160,'NaN'],
                   'Dec':[153,145,141,130,126,120,111,107,103,98,94,89,86,81,78,75,72,69,67,64,63,59,57,56,53,51,49,47,45,43,41]})
 
USGS_Colville_discharge

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Monthly Average Discharge (ft3/s)

variable: 'USGS_Colville_Month_Discharge'
2002-09-01 -> 2021-03-31

website = https://waterdata.usgs.gov/nwis/monthly/?referred_module=sw&site_no=15875000&por_15875000_1200=624322,00060,1200,2002-08,2021-04&format=html_table&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=parameter_selection_list

USGS_Colville_Month_Discharge = pd.DataFrame({'Year':[2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],
                   'Jan':['NaN',4,7,21,18,28,21,15,37,36,29,16,26,45,24,16,25,41,27,22],
                   'Feb':['NaN',0,2,4,0,12,1,0,14,10,11,4,9,29,10,4,12,30,9,8],
                   'Mar':['NaN',0,0,0,0,1,0,0,2,1,2,2,6,17,6,1,7,23,5,3],
                   'Apr':['NaN',0,0,0,0,0,0,3,1,0,1,1,6,12,3,1,6,20,4,3],
                   'May':['NaN',690,40890,12830,22010,4179,17260,36940,17280,37790,16680,6434,33290,62410,47460,12070,12220,36180,25340,'NaN'],
                   'Jun':['NaN',65690,24940,72480,37120,50530,46530,45050,48760,31190,41910,83970,72180,17010,32660,26220,47610,18370,23780,'NaN'],
                   'Jul':['NaN',24030,15310,13920,21940,12140,12900,13890,10370,13170,16970,10530,29820,8243,14540,13110,26970,12380,12270,'NaN'],
                   'Aug':['NaN',31800,24870,4143,33560,17820,10770,13440,15720,11330,14860,10290,10130,22250,27290,36370,30330,38990,19890,'NaN'],
                   'Sep':[21030,12760,12060,6014,6229,7511,1867,13750,6213,11940,27440,11750,16140,11550,15310,25900,23280,15500,23120,'NaN'],
                   'Oct':[7221,10490,557,1169,2667,874,560,1775,1248,1958,3678,1475,1215,1504,4868,6403,3122,3497,6403,'NaN'],
                   'Nov':[844,560,142,200,325,177,207,418,454,375,145,509,217,276,405,448,343,270,370,'NaN'],
                   'Dec':[100,73,57,105,80,73,73,95,132,94,46,131,90,66,64,87,67,79,69,'NaN']})
 
USGS_Colville_Month_Discharge

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Surface-Water Annual Statistics

variable = 'USGS_Colville_annual'

website: https://waterdata.usgs.gov/nwis/annual/?referred_module=sw&site_no=15875000&por_15875000_1200=624322,00060,1200,2002,2021&partial_periods=on&year_type=W&format=html_table&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=parameter_selection_list

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Streamflow Measurements

variable": USGS_Colville_streamflow

website: https://nwis.waterdata.usgs.gov/ak/nwis/measurements/?site_no=15875000&agency_cd=USGS

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Number of tables on site:  1

# Lets go ahead and scrape first table with HTML code gdp[0]
table1 = gdp[0]
 
# the head will form our column names
body = table1.find_all("tr")
 
# Head values (Column names) are the first items of the body list
head = body[0] # 0th item is the header row
body_rows = body[1:] # All other items becomes the rest of the rows
 
# Lets now iterate through the head HTML code and make list of clean headings
 
# Declare empty list to keep Columns names
headings = []
for item in head.find_all("th"): # loop through all th elements
    # convert the th elements to text and strip "\n"
    item = (item.text).rstrip("\n")
    # append the clean column name to headings
    headings.append(item)
    
print(headings)

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['Meas.Number', '\xa0 Date \xa0 \xa0 \xa0 Time', 'TimeDatum', 'MeasurementUsed?', ' \xa0 Who', 'MeasuringAgency', 'Streamflow(ft3/s)', 'GageHeight(ft)', 'GHChange(ft)', 'Meas.Duration(hr)', 'Meas.Rated', ' \xa0 Control', 'FlowAdjust.Code\xa0']

# Next loop though the rest of the rows
 
#print(body_rows[0])
all_rows = [] # will be a list for list for all rows
for row_num in range(len(body_rows)): # A row at a time
    row = [] # this will old entries for one row
    for row_item in body_rows[row_num].find_all("td"): #loop through all row entries
        # row_item.text removes the tags from the entries
        # the following regex is to remove \xa0 and \n and comma from row_item.text
        # xa0 encodes the flag, \n is the newline and comma separates thousands in numbers
        aa = re.sub("(\xa0)|(\n)|,","",row_item.text)
        #append aa to row - note one row entry is being appended
        row.append(aa)
    # append one row to all_rows
    all_rows.append(row)

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# Reformating/ Cleaning 
 
# Fixing the Date/Time column to become numeric
USGS_Colville_streamflow['SampleDatetime'] = pd.to_datetime(USGS_Colville_streamflow['SampleDatetime'])
 
# Changing to numerical 
cols = USGS_Colville_streamflow.columns.drop('SampleDatetime')
 
USGS_Colville_streamflow[cols] = USGS_Colville_streamflow[cols].apply(pd.to_numeric, errors='ignore')
USGS_Colville_streamflow

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# Adding to Other DataFrames
 
# Creating temporary dataframe to append to big Colville Dataframe
temporary = pd.DataFrame(USGS_Colville_streamflow,
                     columns=['SampleDatetime',
                              'Stream_flow(ft3/s)',
                              'Gage_height(ft)',
                              'GH_Change(ft)'])
 
# Changing to numerical 
cols = temporary.columns.drop('SampleDatetime')
temporary[cols] = temporary[cols].apply(pd.to_numeric, errors='ignore')
 
# Add NaN to cells that don't have data
temporary = temporary.replace(' ',np.NaN)
 
temporary

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Stream Historical/Current Data (2016)

variable : 'USGS_Colville_hist'

NOTE: You could probably go back more for this data set but this is set to 2016 to not crash the page

## # Getting the HTML 
url = 'https://nwis.waterdata.usgs.gov/nwis/uv?cb_00020=on&cb_00045=on&cb_00060=on&cb_00065=on&cb_00065=on&format=html&site_no=15875000&period=&begin_date=2016-07-01&end_date=2021-07-14'
 
# Make a GET request to fetch the raw HTML content
html_content = requests.get(url).text
 
# Parse HTML code for the entire site
soup = BeautifulSoup(html_content, "lxml")
 
#print(soup.prettify()) # print the parsed data of html

Number of tables on site:  1

# Lets go ahead and scrape first table with HTML code gdp[0]
table1 = gdp[0]
 
# the head will form our column names
body = table1.find_all("tr")
 
# Head values (Column names) are the first items of the body list
head = body[0] # 0th item is the header row
body_rows = body[1:] # All other items becomes the rest of the rows
 
# Lets now iterate through the head HTML code and make list of clean headings
 
# Declare empty list to keep Columns names
headings = []
for item in head.find_all("th"): # loop through all th elements
    # convert the th elements to text and strip "\n"
    item = (item.text).rstrip("\n")
    # append the clean column name to headings
    headings.append(item)
    
print(headings)

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['Date / Time', '      Dis-charge, ft3/s,', '   Precip-itation total, inches,', '   Temper- ature,  air, deg C,', '      Gageheight,  feet,', '      Gageheight,  feet,[Secondary]']

# Next loop though the rest of the rows
 
#print(body_rows[0])
all_rows = [] # will be a list for list for all rows
for row_num in range(len(body_rows)): # A row at a time
    row = [] # this will old entries for one row
    for row_item in body_rows[row_num].find_all("td"): #loop through all row entries
        # row_item.text removes the tags from the entries
        # the following regex is to remove \xa0 and \n and comma from row_item.text
        # xa0 encodes the flag, \n is the newline and comma separates thousands in numbers
        aa = re.sub("(\xa0)|(\n)|,","",row_item.text)
        #append aa to row - note one row entry is being appended
        row.append(aa)
    # append one row to all_rows
    all_rows.append(row)

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# Dropping the stupid character that don't belong/ keep numbers 
 
for i in ['Discharge_ft2/s',
            'Precipitation_total_in',
            'Temperature_air_C',
            'Gageheight_ft',
            'Gageheight_ft_2']:
  cols[i]= cols[i].str.replace('[^0-9.]','')
  #cols[i]= cols[i].str.replace('P','')
 
# Join datetime back into dataframe
cols = cols.join(USGS_Colville_hist['SampleDatetime'])
#cols
 
# Rename USGS_Kuparuk_hist
USGS_Colville_hist = cols
USGS_Colville_hist

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# Adding to Other DataFrames
 
# Creating temporary dataframe to append to big Colville Dataframe
temporary = pd.DataFrame(USGS_Colville_hist,
                     columns=['SampleDatetime',
            'Discharge_ft2/s',
            'Precipitation_total_in',
            'Temperature_air_C',
            'Gageheight_ft',
            'Gageheight_ft_2'])
 
# Changing to numerical 
cols = temporary.columns.drop('SampleDatetime')
temporary[cols] = temporary[cols].apply(pd.to_numeric, errors='ignore')
 
# Add NaN to cells that don't have data
temporary = temporary.replace(' ',np.NaN)
 
 
# Adding this Dataframe into the big Colville Dataframe
Colville = Colville.append(temporary)
temporary
 

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= All Colville

# From Datetime Column, make new year, month, day, time column 
Colville['Datetime'] = pd.DatetimeIndex(Colville['SampleDatetime'])
Colville['year'] = pd.DatetimeIndex(Colville['SampleDatetime']).year
Colville['month'] = pd.DatetimeIndex(Colville['SampleDatetime']).month
Colville['day'] = pd.DatetimeIndex(Colville['SampleDatetime']).day
Colville['time'] = pd.DatetimeIndex(Colville['SampleDatetime']).time
 
 
Colville.set_index('SampleDatetime',inplace = True)
 
Colville

Code

Text

2.Kuparuk River

USGS: KUPARUK R NR DEADHORSE AK

Variables:

Water Quality Samples = 'USGS_Kuparuk_water'
Monthly Mean Water Temperature = 'USGS_Kuparuk_temperature'
Mean Daily Discharge (ft3/s) = 'USGS_Kuparuk_discharge'
Streamflow Measurements = 'USGS_Kuparuk_discharge'

Latitude 70°16'54", Longitude 148°57'35" NAD27
Drainage area 3,340 square miles
Gage datum 0.00 feet above NGVD29

Code

Text

Water Quality Samples

variable = 'USGS_Kuparuk_water'

# Getting the HTML 
url = 'https://nwis.waterdata.usgs.gov/usa/nwis/qwdata/?site_no=15896000&agency_cd=USGS&inventory_output=0&rdb_inventory_output=gz&TZoutput=0&pm_cd_compare=Greater%20than&radio_parm_cds=all_parm_cds&format=html_table&qw_attributes=expanded&qw_sample_wide=wide&rdb_qw_attributes=0&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=brief_list'
 
# Make a GET request to fetch the raw HTML content
html_content = requests.get(url).text
 
# Parse HTML code for the entire site
soup = BeautifulSoup(html_content, "lxml")
 
#print(soup.prettify()) # print the parsed data of html

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Number of tables on site:  3

# Lets go ahead and scrape first table with HTML code gdp[0]
table1 = gdp[0]
 
# the head will form our column names
body = table1.find_all("tr")
 
# Head values (Column names) are the first items of the body list
head = body[0] # 0th item is the header row
body_rows = body[1:] # All other items becomes the rest of the rows
 
# Lets now iterate through the head HTML code and make list of clean headings
 
# Declare empty list to keep Columns names
headings = []
for item in head.find_all("th"): # loop through all th elements
    # convert the th elements to text and strip "\n"
    item = (item.text).rstrip("\n")
    # append the clean column name to headings
    headings.append(item)
    
print(headings)

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['SampleDatetime', 'Timedatum\xa0', 'Timedatumreliabilitycode\xa0', 'SampleMediumCode\xa0', 'AgencyCollectingSample,Code\xa0', 'Hydro-logicEvent\xa0', 'Hydro-logicCondition\xa0', 'Sampletype\xa0', 'ProjectCode', '     Sam- pling depth,  feet(00003)', '     Stream width,  feet(00004)', ' Loca-tion inX-sect.lookingdwnstrmft from l bank(00009)', '   Temper- ature, water, deg C(00010)', '   Temper- ature,  air, deg C(00020)', ' Expo-  sure time, sample   or test,  days(00022)', '   Baro- metric pres- sure, mm Hg(00025)', '   Agency  ana-lyzingsample,  code(00028)', '    Surface area,  mi2(00049)', '  Instan-taneous  dis-charge, ft3/s(00061)', '      Gageheight,  feet(00065)', '    Turbid-  ity,  JTU(00070)', '    Turbid-  ity,  NTU(00076)', '   Color, water, fltrd, Pt-Co units(00080)', 'Specif-   icconduc- tance,wat unfuS/cm @25 degC(00095)', ' Hydro-  gen  ion, water,unfltrd calcd,  mg/L(00191)', '     Dis- solvedoxygen,  mg/L(00300)', '   Dis- solvedoxygen,percentof sat-uration(00301)', '   pH, water,unfltrd field,  std units(00400)', '   pH, water,unfltrd  lab,  std units(00403)', '   Carbondioxide water,unfltrd  mg/L(00405)', '  ANC,wat unf fixedend pt, field,mg/L as CaCO3(00410)', ' Bicar-bonate,wat unf fixedend pt, field,  mg/L(00440)', 'Carbon-  ate,wat unf fixedend pt, field,  mg/L(00445)', '  Peri- phytonbiomass  ashweight,  g/m2(00572)', '  Peri- phytonbiomass  dryweight,  g/m2(00573)', '  Total nitro-  gen, water,unfltrd  mg/L(00600)', '  Total nitro-  gen, water, fltrd,  mg/L(00602)', ' Organic nitro-  gen, water,unfltrd  mg/L(00605)', ' Organic nitro-  gen, water, fltrd,  mg/L(00607)', '  Ammonia water, fltrd,  mg/L  as N(00608)', '  Ammonia water,unfltrd  mg/L  as N(00610)', '  Nitrite water, fltrd,  mg/L  as N(00613)', '  Nitrate water, fltrd,  mg/L  as N(00618)', 'Ammonia   + org-N, water, fltrd,  mg/L  as N(00623)', 'Ammonia   + org-N,sus sed total,  mg/L  as N(00624)', 'Ammonia   + org-N, water,unfltrd  mg/L  as N(00625)', 'Nitrate   +nitrite waterunfltrd  mg/L  as N(00630)', 'Nitrate   +nitrite water, fltrd,  mg/L  as N(00631)', '   Phos- phate, water,unfltrd  mg/L(00650)', '  Ortho- phos- phate, water, fltrd,  mg/L(00660)', '  Phos-phorus, water,unfltrd  mg/L  as P(00665)', '  Phos-phorus, water, fltrd,  mg/L  as P(00666)', ' Ortho- phos- phate, water, fltrd,  mg/L  as P(00671)', '  Organiccarbon, water,unfltrd  mg/L(00680)', '  Organiccarbon, water, fltrd,  mg/L(00681)', ' Organiccarbon, suspndsedimnt total,  mg/L(00689)', '   Hard- ness, water,mg/L as CaCO3(00900)', 'Noncarb hard- ness,wat unf field,mg/L as CaCO3(00902)', '   Calcium water, fltrd,  mg/L(00915)', '  Magnes-  ium, water, fltrd,  mg/L(00925)', '   Sodium, water, fltrd,  mg/L(00930)', '    Sodiumadsorp-  tion ratio(00931)', '  Sodium frac-  tion   ofcationspercent(00932)', ' Sodium+ potas sium, water, fltrd,  mg/L as Na(00933)', '   Potas- sium, water, fltrd,  mg/L(00935)', '   Chlor-  ide, water, fltrd,  mg/L(00940)', '   Sulfate water, fltrd,  mg/L(00945)', '   Fluor-  ide, water, fltrd,  mg/L(00950)', '  Silica, water, fltrd,mg/L as  SiO2(00955)', '   Arsenic water, fltrd,  ug/L(01000)', '  Arsenic suspndsedimnt total,  ug/L(01001)', '   Arsenic water,unfltrd  ug/L(01002)', '   Barium, water, fltrd,  ug/L(01005)', ' Barium, suspndsedimntrecover -able,  ug/L(01006)', ' Barium, water,unfltrdrecover -able,  ug/L(01007)', '  Beryll-  ium, water, fltrd,  ug/L(01010)', '   Cadmium water, fltrd,  ug/L(01025)', ' Cadmium suspndsedimntrecover -able,  ug/L(01026)', '   Cadmium water,unfltrd  ug/L(01027)', '   Chrom-  ium, water, fltrd,  ug/L(01030)', ' Chrom-  ium, suspndsedimntrecover -able,  ug/L(01031)', ' Chrom-  ium, water,unfltrdrecover -able,  ug/L(01034)', '    Cobalt water, fltrd,  ug/L(01035)', '  Cobalt suspndsedimntrecover -able,  ug/L(01036)', '  Cobalt water,unfltrdrecover -able,  ug/L(01037)', '   Copper, water, fltrd,  ug/L(01040)', ' Copper, suspndsedimntrecover -able,  ug/L(01041)', ' Copper, water,unfltrdrecover -able,  ug/L(01042)', '  Iron, suspndsedimntrecover -able,  ug/L(01044)', '  Iron, water,unfltrdrecover -able,  ug/L(01045)', '    Iron, water, fltrd,  ug/L(01046)', '    Lead, water, fltrd,  ug/L(01049)', '  Lead, suspndsedimntrecover -able,  ug/L(01050)', '  Lead, water,unfltrdrecover -able,  ug/L(01051)', ' Manga- nese, suspndsedimntrecover -able,  ug/L(01054)', ' Manga- nese, water,unfltrdrecover -able,  ug/L(01055)', '   Manga- nese, water, fltrd,  ug/L(01056)', '   Molyb- denum, water, fltrd,  ug/L(01060)', '   Nickel, water, fltrd,  ug/L(01065)', ' Nickel, suspndsedimntrecover -able,  ug/L(01066)', ' Nickel, water,unfltrdrecover -able,  ug/L(01067)', '   Silver, water, fltrd,  ug/L(01075)', ' Silver, suspndsedimntrecover -able,  ug/L(01076)', ' Silver, water,unfltrdrecover -able,  ug/L(01077)', '   Stron- tium, water, fltrd,  ug/L(01080)', '   Vana- dium, water, fltrd,  ug/L(01085)', '    Zinc, water, fltrd,  ug/L(01090)', '  Zinc, suspndsedimntrecover -able,  ug/L(01091)', '  Zinc, water,unfltrdrecover -able,  ug/L(01092)', '   Alum- inum, water, fltrd,  ug/L(01106)', '   Lithium water, fltrd,  ug/L(01130)', '   Selen-  ium, water, fltrd,  ug/L(01145)', '  Selen-  ium, suspndsedimnt total,  ug/L(01146)', '   Selen-  ium, water,unfltrd  ug/L(01147)', '    Gage height above datum meters(30207)', '    Dis-charge,instan-taneous  m3/s(30209)', ' Fecal coli- form,  M-FC0.45uMF  cfu/ 100 mL(31616)', ' Fecal coli- form,  M-FC0.7u MF  cfu/ 100 mL(31625)', ' Fecal strep-tococci   KF  MF,  cfu/ 100 mL(31673)', ' Fecal strep-tococcim-Enterococcus  col/ 100 mL(31679)', ' Chloro-phyll b peri-phyton,uncorr, mg/m2(32226)', ' Chloro-phyll a peri-phyton,uncorr, mg/m2(32228)', ' Biomass peri-phyton,ashfreedrymass  g/m2(49954)', '  Phyto- plank-  ton, total, cells  /mL(60050)', '  Dis- solved solidsdried @180degCwat flt  mg/L(70300)', '  Dis- solvedsolids,sum  ofconsti-tuents,  mg/L(70301)', '    Dis- solvedsolids, water, ton/d(70302)', '   Dis- solvedsolids, water, tons/acre-ft(70303)', 'Suspnd.sedimnt sievediametrpercent<0.0625   mm(70331)', 'Suspnd. sedi- ment, sievediametrpercent<.125mm(70332)', 'Suspnd. sedi- ment, sievediametrpercent <.25mm(70333)', 'Suspnd. sedi- ment, sievediametrpercent <.5 mm(70334)', 'Suspnd. sedi- ment,falldiadst watpercent<.002mm(70337)', 'Suspnd. sedi- ment,falldiadst watpercent<.004mm(70338)', 'Suspnd. sedi- ment,falldiadst watpercent<.008mm(70339)', 'Suspnd. sedi- ment,falldiadst watpercent<.016mm(70340)', 'Suspnd. sedi- ment,falldiadst watpercent<.031mm(70341)', ' Ortho- phos- phate, water,unfltrd  mg/L  as P(70507)', 'Biomasschloro- phyll ratio, peri-phyton, number(70950)', 'Chloro-phyll a peri-phyton,chromo-fluoro, mg/m2(70957)', 'Chloro-phyll b peri-phyton,chromo-fluoro, mg/m2(70958)', '   Ammonia water,unfltrd  mg/L(71845)', '   Ammonia water, fltrd,  mg/L(71846)', '   Nitrate water, fltrd,  mg/L(71851)', '  Nitrite water, fltrd,  mg/L as NO2(71856)', '   Manga- nese, water,unfltrd  ug/L(71883)', '    Iron, water,unfltrd  ug/L(71885)', '  Phos-phorus, water,unfltrd  mg/L as PO4(71886)', ' Total nitro-  gen, water,unfltrd  mg/L as NO3(71887)', '   Mercury water, fltrd,  ug/L(71890)', ' Mercury suspndsedimntrecover -able,  ug/L(71895)', ' Mercury water,unfltrdrecover -able,  ug/L(71900)', '     Samplepurpose  code(71999)', ' Sample  loc- ation, crosssectionft fromrt bank(72103)', '  Sus- pended sedi-  mentconcen-tration  mg/L(80154)', '  Sus- pended sedi-  ment  dis-charge, ton/d(80155)', '    K-40, water, fltrd, pCi/L(82068)', '     Sam- plingmethod,  code(82398)', 'Specif.conduc- tance,wat unf  lab,uS/cm @25 degC(90095)', '  ANC,wat unf fixedend pt,  lab,mg/L as CaCO3(90410)', '     Con-versionfactor, number(95100)', '   Total  cell count, cells/   mL(95200)', ' Noncarb hard- ness, water,mg/L as CaCO3(95902)', '    CHLAMYDOMONAS#/mL(96014)', '    RPTEROMONAS#/mL(96029)', '    CHLOROCOCCUM#/mL(96167)', '    SCHROEDERIA#/mL(96190)', '   ANKISTRODESMUS#/mL(96202)', '    SCENEDESMUS#/mL(96243)', '    TETRASTRUM#/mL(96245)', '    GOLENKINIA#/mL(96252)', '    MOUGEOTIA#/mL(96294)', '     ZYGNEMA#/mL(96299)', '    COSMARIUM#/mL(96313)', '    TRACHELOMONAS#/mL(96392)', '    CRYPTOMONAS#/mL(96416)', '    CRYPTOMONAS#/mL(96430)', '    GYMNODINIUM#/mL(96454)', '    PERIDINIUM#/mL(96487)', '    MALLOMONAS#/mL(96595)', '    DINOBYRON#/mL(96605)', '    OCHROMONAS#/mL(96608)', '    CYCLOTELLA#/mL(96706)', '    MELOSIRA#/mL(96707)', '    TABELLARIA#/mL(96750)', '     DIATOMA#/mL(96759)', '    ASTERIONELLA#/mL(96763)', '    FRAGILARIA#/mL(96764)', '    SYNEDRA#/mL(96765)', '     HANNAEA#/mL(96766)', '     H.ARCUS#/mL(96767)', '     EUNOTIA#/mL(96775)', '    ACHNANTHES#/mL(96777)', '    NAVICULA#/mL(96791)', '    NEIDIUM#/mL(96792)', '    PINNULARIA#/mL(96793)', '    STAURONEIS#/mL(96795)', '    GOMPHONEMA#/mL(96802)', '     AMPHORA#/mL(96805)', '    CYMBELLA#/mL(96806)', '    NITZSCHIA#/mL(96812)', '    SURIRELLA#/mL(96817)', '    ANACYSTIS#/mL(98082)', '    AGMENELLUM#/mL(98090)', '    OSCILLATORIA#/mL(98136)', '    SCHIZOTHRIX#/mL(98150)', '    ANABAENA#/mL(98165)', '    APHANIZOMENON#/mL(98167)', 'Carbon-  ate,wat unfinf tit field,mg/L as CaCO3(99430)', ' Bicar-bonate,wat unfinfl pt titr., field,  mg/L(99440)']

# Next loop though the rest of the rows
 
#print(body_rows[0])
all_rows = [] # will be a list for list for all rows
for row_num in range(len(body_rows)): # A row at a time
    row = [] # this will old entries for one row
    for row_item in body_rows[row_num].find_all("td"): #loop through all row entries
        # row_item.text removes the tags from the entries
        # the following regex is to remove \xa0 and \n and comma from row_item.text
        # xa0 encodes the flag, \n is the newline and comma separates thousands in numbers
        aa = re.sub("(\xa0)|(\n)|,","",row_item.text)
        #append aa to row - note one row entry is being appended
        row.append(aa)
    # append one row to all_rows
    all_rows.append(row)

Enter to Rename, ⇧Enter to Preview

# Renaming Columns ( bc they were gross before)
 
headings = ['SampleDatetime',
 'Time datum',
 'Time datum reliability code',
 'Sample Medium Code',
 'Agency Collecting Sample, Code',
 'Hydrologic Event',
 'Hydrologic Condition',
 'Sample type',
 'ProjectCode',
 'Sampling depth (ft)(00003)',
 'Stream width (ft)(00004)',
 'Location intersect (lookingdwnstream) from l bank)',
 'Temperature,water(C)(00010)',
 'Temperature,air(C)(00020)',
 'Exposure time, sample or test, days(00022)',
 'Barometric pressure, (mmHg)(00025)',
 'Agency analyzing sample, code(00028)',
 'Surface area,(mi2)(00049)',
 'Instantaneous_discharge(ft3/s)(00061)',
 'Gageheight_feet(00065)',
 'Turbidity,JTU(00070)',
 'Turbidity,NTU(00076)',
 'Color, water, fltrd, Pt-Co units(00080)',
 'Specific conductance,wat unfuS/cm @25 degC(00095)',
 'Hydrogen_ion, water,unfltrd calcd,  mg/L(00191)',
 'Dissolved_oxygen,(mg/L)(00300)',
 'Dissolved_oxygen,percentof sat-uration(00301)',
 'pH, water,unfltrd field, std units(00400)',
 'pH, water,unfltrd lab, std units(00403)',
 'Carbon_dioxide water,unfltrd  mg/L(00405)',
 'ANC,wat unf fixedend pt, field,mg/L as CaCO3(00410)',
 'Bicarbonate,wat unf fixedend pt, field,  mg/L(00440)',
 'Carbonate,wat unf fixedend pt, field,  mg/L(00445)',
 'Peri-phytonbiomass ashweight, g/m2(00572)',
 'Peri-phytonbiomass dryweight, g/m2(00573)',
 'Total nitrogen, water,unfltrd mg/L(00600)',
 'Total nitrogen, water, fltrd, mg/L(00602)',
 'Organic nitrogen, water,unfltrd  mg/L(00605)',
 'Organic nitrogen, water, fltrd,  mg/L(00607)',
 'Ammonia water, fltrd,  mg/L  as N(00608)',
 'Ammonia water,unfltrd  mg/L  as N(00610)',
 'Nitrite water, fltrd, mg/L  as N(00613)',
 'Nitrate water, fltrd,  mg/L  as N(00618)',
 'Ammonia + org-N, water, fltrd, mg/L  as N(00623)',
 'Ammonia + org-N,sus sed total, mg/L  as N(00624)',
 'Ammonia + org-N, water,unfltrd mg/L  as N(00625)',
 'Nitrate + nitrite water,unfltrd mg/L  as N(00630)',
 'Nitrate + nitrite water, fltrd, mg/L  as N(00631)',
 'Phosphate, water,unfltrd mg/L(00650)',
 'Orthophosphate, water, fltrd, mg/L(00660)',
 'Phosphorus, water,unfltrd mg/L  as P(00665)',
 'Phosphorus, water, fltrd, mg/L  as P(00666)',
 'Orthophosphate, water, fltrd, mg/L  as P(00671)',
 'Organiccarbon, water,unfltrd mg/L(00680)',
 'Organiccarbon, water, fltrd, mg/L(00681)',
 'Organiccarbon, suspndsedimnt total, mg/L(00689)',
 'Hardness, water,mg/L as CaCO3(00900)',
 'Noncarb hardness,wat unf field,mg/L as CaCO3(00902)',
 'Calcium water, fltrd, mg/L(00915)',
 'Magnesium, water, fltrd, mg/L(00925)',
 'Sodium, water, fltrd, mg/L(00930)',
 'Sodium adsorption ratio(00931)',
 'Sodium fraction of cations percent(00932)',
 'Sodium + potassium, water, fltrd, mg/L as Na(00933)',
 'Potassium, water, fltrd, mg/L(00935)',
 'Chloride, water, fltrd, mg/L(00940)',
 'Sulfate water, fltrd, mg/L(00945)',
 'Fluoride, water, fltrd, mg/L(00950)',
 'Silica, water, fltrd,mg/L as  SiO2(00955)',
 'Arsenic water, fltrd, ug/L(01000)',
 'Arsenic suspndsedimnt total,  ug/L(01001)',
 'Arsenic water,unfltrd ug/L(01002)',
 'Barium, water, fltrd, ug/L(01005)',
 'Barium, suspndsedimntrecover -able,  ug/L(01006)',
 'Barium, water,unfltrdrecover -able,  ug/L(01007)',
 'Beryllium, water, fltrd, ug/L(01010)',
 'Cadmium water, fltrd, ug/L(01025)',
 'Cadmium suspndsedimntrecover -able,  ug/L(01026)',
 'Cadmium water,unfltrd ug/L(01027)',
 'Chromium, water, fltrd,  ug/L(01030)',
 'Chromium, suspndsedimntrecover -able,  ug/L(01031)',
 'Chromium, water,unfltrdrecover -able,  ug/L(01034)',
 'Cobalt water, fltrd,  ug/L(01035)',
 'Cobalt suspndsedimntrecover -able,  ug/L(01036)',
 'Cobalt water,unfltrdrecover -able,  ug/L(01037)',
 'Copper, water, fltrd,  ug/L(01040)',
 'Copper, suspndsedimntrecover -able,  ug/L(01041)',
 'Copper, water,unfltrdrecover -able,  ug/L(01042)',
 'Iron, suspndsedimntrecover -able,  ug/L(01044)',
 'Iron, water,unfltrdrecover -able,  ug/L(01045)',
 'Iron, water, fltrd,  ug/L(01046)',
 'Lead, water, fltrd,  ug/L(01049)',
 'Lead, suspndsedimntrecover -able,  ug/L(01050)',
 'Lead, water,unfltrdrecover -able,  ug/L(01051)',
 'Manganese, suspndsedimntrecover -able,  ug/L(01054)',
 'Manganese, water,unfltrdrecover -able,  ug/L(01055)',
 'Manganese, water, fltrd,  ug/L(01056)',
 'Molybdenum, water, fltrd,  ug/L(01060)',
 'Nickel, water, fltrd,  ug/L(01065)',
 'Nickel, suspndsedimntrecover -able,  ug/L(01066)',
 'Nickel, water,unfltrdrecover -able,  ug/L(01067)',
 'Silver, water, fltrd,  ug/L(01075)',
 'Silver, suspndsedimntrecover -able,  ug/L(01076)',
 'Silver, water,unfltrdrecover -able,  ug/L(01077)',
 'Strontium, water, fltrd,  ug/L(01080)',
 'Vanadium, water, fltrd,  ug/L(01085)',
 'Zinc, water, fltrd,  ug/L(01090)',
 'Zinc, suspndsedimntrecover -able,  ug/L(01091)',
 'Zinc, water,unfltrdrecover -able,  ug/L(01092)',
 'Aluminum, water, fltrd,  ug/L(01106)',
 'Lithium water, fltrd,  ug/L(01130)',
 'Selenium, water, fltrd,  ug/L(01145)',
 'Selenium, suspndsedimnt total,  ug/L(01146)',
 'Selenium, water,unfltrd  ug/L(01147)',
 'Gage height above datum meters(30207)',
 'Discharge,instantaneous  m3/s(30209)',
 'Fecal coli- form,  M-FC0.45uMF  cfu/ 100 mL(31616)',
 'Fecal coli- form,  M-FC0.7u MF  cfu/ 100 mL(31625)',
 'Fecal strep-tococci   KF  MF,  cfu/ 100 mL(31673)',
 'Fecal strep-tococcim-Enterococcus  col/ 100 mL(31679)',
 'Chlorophyll b periphyton,uncorr, mg/m2(32226)',
 'Chlorophyll a periphyton,uncorr, mg/m2(32228)',
 'Biomass peri-phyton,ashfreedrymass  g/m2(49954)',
 'Phytoplankton, total, cells  /mL(60050)',
 'Dissolved solids,dried @180degCwat flt  mg/L(70300)',
 'Dissolved solids,sum  ofconsti-tuents,  mg/L(70301)',
 'Dissolved solids,water, ton/d(70302)',
 'Dissolved solids,water, tons/acre-ft(70303)',
 'Suspnd. sediment sievediametrpercent<0.0625   mm(70331)',
 'Suspnd. sediment, sievediametrpercent<.125mm(70332)',
 'Suspnd. sediment, sievediametrpercent <.25mm(70333)',
 'Suspnd. sediment, sievediametrpercent <.5 mm(70334)',
 'Suspnd. sediment,falldiadst watpercent<.002mm(70337)',
 'Suspnd. sediment,falldiadst watpercent<.004mm(70338)',
 'Suspnd. sediment,falldiadst watpercent<.008mm(70339)',
 'Suspnd. sediment,falldiadst watpercent<.016mm(70340)',
 'Suspnd. sediment,falldiadst watpercent<.031mm(70341)',
 'Orthophosphate, water,unfltrd  mg/L  as P(70507)',
 'Biomasschlorophyll ratio, peri-phyton, number(70950)',
 'Chloro-phyll a periphyton,chromo-fluoro, mg/m2(70957)',
 'Chloro-phyll b periphyton,chromo-fluoro, mg/m2(70958)',
 'Ammonia water, unfltrd  mg/L(71845)',
 'Ammonia water, fltrd,  mg/L(71846)',
 'Nitrate water, fltrd,  mg/L(71851)',
 'Nitrite water, fltrd,  mg/L as NO2(71856)',
 'Manga- nese, water,unfltrd  ug/L(71883)',
 'Iron, water,unfltrd  ug/L(71885)',
 'Phosphorus, water,unfltrd  mg/L as PO4(71886)',
 'Total nitrogen, water,unfltrd  mg/L as NO3(71887)',
 'Mercury water, fltrd,  ug/L(71890)',
 'Mercury suspndsedimntrecover -able,  ug/L(71895)',
 'Mercury water,unfltrdrecover -able,  ug/L(71900)',
 'Sample purpose  code(71999)',
 'Sample location, crosssection ft fromrt bank(72103)',
 'Suspended sediment concentration  mg/L(80154)',
 'Suspended sediment  discharge, ton/d(80155)',
 'K-40, water, fltrd, pCi/L(82068)',
 'Sam- plingmethod, code(82398)',
 'Specif.conduc- tance,wat unf  lab,uS/cm @25 degC(90095)',
 'ANC,wat unf fixedend pt,  lab,mg/L as CaCO3(90410)',
 'Conversionfactor, number(95100)',
 'Total cell count, cells/   mL(95200)',
 'Noncarb hardness, water,mg/L as CaCO3(95902)',
 'CHLAMYDOMONAS#/mL(96014)',
 'RPTEROMONAS#/mL(96029)',
 'CHLOROCOCCUM#/mL(96167)',
 'SCHROEDERIA#/mL(96190)',
 'ANKISTRODESMUS#/mL(96202)',
 'SCENEDESMUS#/mL(96243)',
 'TETRASTRUM#/mL(96245)',
 'GOLENKINIA#/mL(96252)',
 'MOUGEOTIA#/mL(96294)',
 'ZYGNEMA#/mL(96299)',
 'COSMARIUM#/mL(96313)',
 'TRACHELOMONAS#/mL(96392)',
 'CRYPTOMONAS#/mL(96416)',
 'CRYPTOMONAS#/mL(96430)',
 'GYMNODINIUM#/mL(96454)',
 'PERIDINIUM#/mL(96487)',
 'MALLOMONAS#/mL(96595)',
 'DINOBYRON#/mL(96605)',
 'OCHROMONAS#/mL(96608)',
 'CYCLOTELLA#/mL(96706)',
 'MELOSIRA#/mL(96707)',
 'TABELLARIA#/mL(96750)',
 'DIATOMA#/mL(96759)',
 'ASTERIONELLA#/mL(96763)',
 'FRAGILARIA#/mL(96764)',
 'SYNEDRA#/mL(96765)',
 'HANNAEA#/mL(96766)',
 'H.ARCUS#/mL(96767)',
 'EUNOTIA#/mL(96775)',
 'ACHNANTHES#/mL(96777)',
 'NAVICULA#/mL(96791)',
 'NEIDIUM#/mL(96792)',
 'PINNULARIA#/mL(96793)',
 'STAURONEIS#/mL(96795)',
 'GOMPHONEMA#/mL(96802)',
 'AMPHORA#/mL(96805)',
 'CYMBELLA#/mL(96806)',
 'NITZSCHIA#/mL(96812)',
 'SURIRELLA#/mL(96817)',
 'ANACYSTIS#/mL(98082)',
 'AGMENELLUM#/mL(98090)',
 'OSCILLATORIA#/mL(98136)',
 'SCHIZOTHRIX#/mL(98150)',
 'ANABAENA#/mL(98165)',
 'APHANIZOMENON#/mL(98167)',
 'Carbonate,wat unfinf tit field,mg/L as CaCO3(99430)',
 'Bicarbonate,wat unfinfl pt titr., field, mg/L(99440)']

Enter to Rename, ⇧Enter to Preview

SampleDatetime                                          datetime64[ns]
Time datum                                                      object
Time datum reliability code                                     object
Sample Medium Code                                              object
Agency Collecting Sample, Code                                  object
                                                             ...      
SCHIZOTHRIX#/mL(98150)                                          object
ANABAENA#/mL(98165)                                             object
APHANIZOMENON#/mL(98167)                                        object
Carbonate,wat unfinf tit field,mg/L as CaCO3(99430)            float64
Bicarbonate,wat unfinfl pt titr., field, mg/L(99440)           float64
Length: 210, dtype: object

# Adding to Other DataFrames
 
# Adding this Dataframe into the big Sagavanirtok Dataframe
Kuparuk = pd.DataFrame(USGS_Kuparuk_water,
                       columns=['SampleDatetime',
 'Sampling depth (ft)(00003)',
 'Stream width (ft)',
 'Temperature,water(C)(00010)',
 'Temperature,air(C)(00020)',
 'Barometric pressure, (mmHg)(00025)',
 'Surface area,(mi2)(00049)',
 'Instantaneous_discharge(ft3/s)(00061)',
 'Gageheight_feet(00065)',
 'Turbidity,JTU(00070)',
 'Turbidity,NTU(00076)',
 'Color, water, fltrd, Pt-Co units(00080)',
 'Specific conductance,wat unfuS/cm @25 degC(00095)',
 'Hydrogen_ion, water,unfltrd calcd,  mg/L(00191)',
 'Dissolved_oxygen,(mg/L)(00300)',
 'Dissolved_oxygen,percentof sat-uration(00301)',
 'pH, water,unfltrd field, std units(00400)',
 'pH, water,unfltrd lab, std units(00403)',
 'Carbon_dioxide water,unfltrd  mg/L(00405)',
 'Bicarbonate,wat unf fixedend pt, field,  mg/L(00440)',
 'Carbonate,wat unf fixedend pt, field,  mg/L(00445)',
 'Peri-phytonbiomass ashweight, g/m2(00572)',
 'Peri-phytonbiomass dryweight, g/m2(00573)',
 'Total nitrogen, water,unfltrd mg/L(00600)',
 'Total nitrogen, water, fltrd, mg/L(00602)',
 'Organic nitrogen, water,unfltrd  mg/L(00605)',
 'Organic nitrogen, water, fltrd,  mg/L(00607)',
 'Ammonia water, fltrd,  mg/L  as N(00608)',
 'Ammonia water,unfltrd  mg/L  as N(00610)',
 'Nitrite water, fltrd, mg/L  as N(00613)',
 'Nitrate water, fltrd,  mg/L  as N(00618)',
 'Ammonia + org-N, water, fltrd, mg/L  as N(00623)',
 'Ammonia + org-N,sus sed total, mg/L  as N(00624)',
 'Ammonia + org-N, water,unfltrd mg/L  as N(00625)',
 'Nitrate + nitrite water,unfltrd mg/L  as N(00630)',
 'Nitrate + nitrite water, fltrd, mg/L  as N(00631)',
 'Phosphate, water,unfltrd mg/L(00650)',
 'Orthophosphate, water, fltrd, mg/L(00660)',
 'Phosphorus, water,unfltrd mg/L  as P(00665)',
 'Phosphorus, water, fltrd, mg/L  as P(00666)',
 'Orthophosphate, water, fltrd, mg/L  as P(00671)',
 'Organiccarbon, water,unfltrd mg/L(00680)',
 'Organiccarbon, water, fltrd, mg/L(00681)',
 'Organiccarbon, suspndsedimnt total, mg/L(00689)',
 'Hardness, water,mg/L as CaCO3(00900)',
 'Noncarb hardness,wat unf field,mg/L as CaCO3(00902)',
 'Calcium water, fltrd, mg/L(00915)',
 'Magnesium, water, fltrd, mg/L(00925)',
 'Sodium, water, fltrd, mg/L(00930)',
 'Sodium adsorption ratio(00931)',
 'Sodium fraction of cations percent(00932)',
 'Sodium + potassium, water, fltrd, mg/L as Na(00933)',
 'Potassium, water, fltrd, mg/L(00935)',
 'Chloride, water, fltrd, mg/L(00940)',
 'Sulfate water, fltrd, mg/L(00945)',
 'Fluoride, water, fltrd, mg/L(00950)',
 'Silica, water, fltrd,mg/L as  SiO2(00955)',
 'Arsenic water, fltrd, ug/L(01000)',
 'Arsenic suspndsedimnt total,  ug/L(01001)',
 'Arsenic water,unfltrd ug/L(01002)',
 'Barium, water, fltrd, ug/L(01005)',
 'Barium, suspndsedimntrecover -able,  ug/L(01006)',
 'Barium, water,unfltrdrecover -able,  ug/L(01007)',
 'Beryllium, water, fltrd, ug/L(01010)',
 'Cadmium water, fltrd, ug/L(01025)',
 'Cadmium suspndsedimntrecover -able,  ug/L(01026)',
 'Cadmium water,unfltrd ug/L(01027)',
 'Chromium, water, fltrd,  ug/L(01030)',
 'Chromium, suspndsedimntrecover -able,  ug/L(01031)',
 'Chromium, water,unfltrdrecover -able,  ug/L(01034)',
 'Cobalt water, fltrd,  ug/L(01035)',
 'Cobalt suspndsedimntrecover -able,  ug/L(01036)',
 'Cobalt water,unfltrdrecover -able,  ug/L(01037)',
 'Copper, water, fltrd,  ug/L(01040)',
 'Copper, suspndsedimntrecover -able,  ug/L(01041)',
 'Copper, water,unfltrdrecover -able,  ug/L(01042)',
 'Iron, suspndsedimntrecover -able,  ug/L(01044)',
 'Iron, water,unfltrdrecover -able,  ug/L(01045)',
 'Iron, water, fltrd,  ug/L(01046)',
 'Lead, water, fltrd,  ug/L(01049)',
 'Lead, suspndsedimntrecover -able,  ug/L(01050)',
 'Lead, water,unfltrdrecover -able,  ug/L(01051)',
 'Manganese, suspndsedimntrecover -able,  ug/L(01054)',
 'Manganese, water,unfltrdrecover -able,  ug/L(01055)',
 'Manganese, water, fltrd,  ug/L(01056)',
 'Molybdenum, water, fltrd,  ug/L(01060)',
 'Nickel, water, fltrd,  ug/L(01065)',
 'Nickel, suspndsedimntrecover -able,  ug/L(01066)',
 'Nickel, water,unfltrdrecover -able,  ug/L(01067)',
 'Silver, water, fltrd,  ug/L(01075)',
 'Silver, suspndsedimntrecover -able,  ug/L(01076)',
 'Silver, water,unfltrdrecover -able,  ug/L(01077)',
 'Strontium, water, fltrd,  ug/L(01080)',
 'Vanadium, water, fltrd,  ug/L(01085)',
 'Zinc, water, fltrd,  ug/L(01090)',
 'Zinc, suspndsedimntrecover -able,  ug/L(01091)',
 'Zinc, water,unfltrdrecover -able,  ug/L(01092)',
 'Aluminum, water, fltrd,  ug/L(01106)',
 'Lithium water, fltrd,  ug/L(01130)',
 'Selenium, water, fltrd,  ug/L(01145)',
 'Selenium, suspndsedimnt total,  ug/L(01146)',
 'Selenium, water,unfltrd  ug/L(01147)',
 'Gage height above datum meters(30207)',
 'Discharge,instantaneous  m3/s(30209)',
 'Fecal coli- form,  M-FC0.45uMF  cfu/ 100 mL(31616)',
 'Fecal coli- form,  M-FC0.7u MF  cfu/ 100 mL(31625)',
 'Fecal strep-tococci   KF  MF,  cfu/ 100 mL(31673)',
 'Fecal strep-tococcim-Enterococcus  col/ 100 mL(31679)',
 'Chlorophyll b periphyton,uncorr, mg/m2(32226)',
 'Chlorophyll a periphyton,uncorr, mg/m2(32228)',
 'Biomass peri-phyton,ashfreedrymass  g/m2(49954)',
 'Phytoplankton, total, cells  /mL(60050)',
 'Dissolved solids,dried @180degCwat flt  mg/L(70300)',
 'Dissolved solids,sum  ofconsti-tuents,  mg/L(70301)',
 'Dissolved solids,water, ton/d(70302)',
 'Dissolved solids,water, tons/acre-ft(70303)',
 'Suspnd. sediment sievediametrpercent<0.0625   mm(70331)',
 'Suspnd. sediment, sievediametrpercent<.125mm(70332)',
 'Suspnd. sediment, sievediametrpercent <.25mm(70333)',
 'Suspnd. sediment, sievediametrpercent <.5 mm(70334)',
 'Suspnd. sediment,falldiadst watpercent<.002mm(70337)',
 'Suspnd. sediment,falldiadst watpercent<.004mm(70338)',
 'Suspnd. sediment,falldiadst watpercent<.008mm(70339)',
 'Suspnd. sediment,falldiadst watpercent<.016mm(70340)',
 'Suspnd. sediment,falldiadst watpercent<.031mm(70341)',
 'Orthophosphate, water,unfltrd  mg/L  as P(70507)',
 'Biomasschlorophyll ratio, peri-phyton, number(70950)',
 'Chloro-phyll a periphyton,chromo-fluoro, mg/m2(70957)',
 'Chloro-phyll b periphyton,chromo-fluoro, mg/m2(70958)',
 'Ammonia water, unfltrd  mg/L(71845)',
 'Ammonia water, fltrd,  mg/L(71846)',
 'Nitrate water, fltrd,  mg/L(71851)',
 'Nitrite water, fltrd,  mg/L as NO2(71856)',
 'Manga- nese, water,unfltrd  ug/L(71883)',
 'Iron, water,unfltrd  ug/L(71885)',
 'Phosphorus, water,unfltrd  mg/L as PO4(71886)',
 'Total nitrogen, water,unfltrd  mg/L as NO3(71887)',
 'Mercury water, fltrd,  ug/L(71890)',
 'Mercury suspndsedimntrecover -able,  ug/L(71895)',
 'Mercury water,unfltrdrecover -able,  ug/L(71900)',
 'Suspended sediment concentration  mg/L(80154)',
 'Suspended sediment  discharge, ton/d(80155)',
 'Carbonate,wat unfinf tit field,mg/L as CaCO3(99430)',
 'Bicarbonate,wat unfinfl pt titr., field, mg/L(99440)'])
 

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Mean Daily Discharge (ft3/s)

variable = 'USGS_Kuparuk_discharge'
Calculation Period 1970-10-01 -> 2021-09-30

website: https://waterdata.usgs.gov/nwis/dvstat?referred_module=sw&site_no=15896000&por_15896000_1204=624324,00010,1204,1978-07-27,2020-09-22&site_no=15896000&por_15896000_1205=624324,00060,1205,1971-05-31,2020-10-05&format=html_table&stat_cds=mean_va&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=parameter_selection_list

USGS_Kuparuk_discharge = pd.DataFrame({'Day':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],
                   'Jan':[0.48,0.44,0.39,0.35,0.34,0.32,0.29,0.19,0.17,0.14,0.11,0.11,0.1,0.07,0.03,0.03,0.03,0.03,0.03,0.02,0.02,0.02,0.02,0.02,0.02,0.01,0.01,0.01,0.01,0.01,0.01],
                   'Feb':[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,'NaN','NaN'],
                   'Mar':[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
                   'Apr':[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,'NaN'],
                   'May':[0,0,0,0,0,0,0,0,0,2,12,24,40,65,257,270,318,462,1170,3470,3450,3310,3810,4960,5600,6230,6500,7140,8930,10300,11600],
                   'Jun':[12700,13300,15400,17600,19700,21600,23200,21600,19800,18800,16900,14700,12300,10600,9940,8490,6900,5600,4810,4210,3830,3500,3260,2980,2920,2760,2450,2230,2110,1970,'NaN'],
                   'Jul':[1810,1610,1520,1560,1620,1690,1490,1370,1380,1350,1250,1180,1170,1080,988,969,1020,983,1040,1280,1220,1180,1310,1320,1390,1370,1240,1170,1140,1180,1170],
                   'Aug':[1070,1170,1270,1250,1380,1530,1470,1350,1310,1350,1450,1450,1390,1430,1700,2250,2680,2810,2620,2420,2320,2410,2360,2360,2300,2170,2240,2790,3260,2950,2640],
                   'Sep':[2440,2530,2970,2990,2880,2850,2770,2680,2520,2430,2340,2210,2110,2040,1990,1920,1790,1650,1530,1440,1370,1310,1250,1270,1270,1210,1140,1070,1010,1000,'NaN'],
                   'Oct':[995,932,954,953,879,784,710,648,581,518,466,419,377,340,310,286,264,244,223,207,189,175,162,148,137,127,116,106,96,88,80],
                   'Nov':[73,67,61,57,53,48,45,41,37,34,31,28,26,25,22,21,19,18,17,16,15,14,13,13,12,11,10,10,10,9,'NaN'],
                   'Dec':[8,8,7,7,6,6,5,5,5,4,4,4,3,3,3,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1]})
 
USGS_Kuparuk_discharge

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Monthly Average Discharge (ft3/s)

variable: 'USGS_Kuparuk_Month_Discharge'

1971-06-01 -> 2020-09-30

website:'https://waterdata.usgs.gov/nwis/monthly/?referred_module=sw&site_no=15896000&por_15896000_1205=624324,00060,1205,1971-05,2020-10&format=html_table&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=parameter_selection_list'

USGS_Kuparuk_Month_Discharge = pd.DataFrame({'Year':[1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020],
                   'Jan':['NaN','NaN','NaN','NaN',0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0.581,0,0,0,0,0.129,0,0,0.065,0.742,0.032,0,0,1.65,0,1.71],
                   'Feb':['NaN','NaN','NaN','NaN',0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
                   'Mar':['NaN','NaN','NaN','NaN',0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
                   'Apr':['NaN','NaN','NaN','NaN',0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
                   'May':['NaN','NaN','NaN','NaN',0.0,0.0,0.0,0.0,2784.0,107.1,2514.0,51.3,0.0,26.8,1818.0,9.0,1398.0,1642.0,11.0,6522.0,1960.0,193.9,1903.0,935.5,4058.0,8877.0,2411.0,6877.0,1198.0,0.0,0.0,6752.0,3.6,3554.0,1066.0,3910.0,16.2,1418.0,5549.0,1135.0,'NaN',1841.0,9.7,4074.0,15720.0,8145.0,176.6,126.0,7393.0,5082.0],
                   'Jun':[17880.0,12360.0,16070.0,5592.0,8396.0,14710.0,15940.0,15100.0,3496.0,13340.0,8755.0,26360.0,10740.0,10240.0,4466.0,14870.0,6563.0,10700.0,16190.0,725.6,8107.0,9811.0,10960.0,6545.0,8429.0,6370.0,14560.0,3493.0,7066.0,15130.0,9715.0,2434.0,8723.0,9444.0,11410.0,5981.0,8218.0,5475.0,9677.0,11790.0,7428.0,8467.0,22070.0,17910.0,4230.0,7876.0,8233.0,17870.0,4024.0,6429.0],
                   'Jul':[299.9,720.7,1392.0,869.7,746.3,2131.0,1207.0,632.7,994.5,955.9,2439.0,667.8,310.0,1240.0,1034.0,550.7,357.4,430.3,1193.0,375.6,612.3,881.1,690.2,1404.0,2293.0,1050.0,1378.0,451.7,3169.0,2515.0,957.0,1794.0,3309.0,2433.0,1314.0,1987.0,260.3,749.2,935.9,499.2,482.7,480.6,1702.0,4949.0,220.3,1207.0,1696.0,4506.0,1355.0,785.6],
                   'Aug':[426.4,2641.0,1777.0,749.0,197.1,212.7,138.4,179.4,3135.0,2678.0,2174.0,306.7,333.9,4700.0,159.0,1616.0,1895.0,1204.0,5095.0,126.9,717.9,2112.0,1036.0,2612.0,1930.0,1305.0,3367.0,881.2,900.3,1474.0,4582.0,5229.0,3427.0,3181.0,247.7,2275.0,154.0,1080.0,1293.0,1622.0,319.4,912.9,1810.0,1852.0,1843.0,2835.0,5784.0,4821.0,7481.0,1811.0],
                   'Sep':[219.2,2738.0,914.4,192.5,621.4,251.0,683.5,251.2,1561.0,1974.0,964.4,419.5,1235.0,988.0,1083.0,1117.0,3607.0,1045.0,3062.0,1907.0,703.5,1546.0,2423.0,3429.0,1311.0,1738.0,4863.0,1737.0,2399.0,655.8,1270.0,3238.0,2361.0,2704.0,134.0,1106.0,78.3,234.7,1863.0,750.1,1020.0,2910.0,2474.0,3638.0,2355.0,4284.0,5905.0,8674.0,3794.0,2202.0],
                   'Oct':['NaN','NaN','NaN',10.0,43.1,40.6,691.6,56.6,212.9,91.6,177.4,106.9,58.6,152.7,250.3,242.9,261.6,87.9,425.8,310.5,130.6,273.5,444.5,420.9,240.2,226.9,461.9,420.0,425.8,153.4,316.9,1675.0,503.9,532.9,51.6,410.7,13.8,64.4,596.3,254.4,178.1,1219.0,350.2,1101.0,195.2,908.1,2314.0,745.1,690.6,'NaN'],
                   'Nov':['NaN','NaN','NaN',5.0,0.4,0.0,13.3,1.1,0.0,0.0,8.3,3.9,0.0,8.7,40.0,5.7,26.0,0.8,0.0,0.1,3.4,52.7,51.0,7.4,32.7,8.9,33.7,43.9,24.4,17.9,34.7,65.8,92.7,105.7,4.3,46.7,1.8,16.7,65.7,25.2,7.7,43.5,42.8,108.0,33.3,51.3,105.3,16.7,56.0,'NaN'],
                   'Dec':['NaN','NaN','NaN',1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.5,0.0,0.0,0.0,0.0,0.0,0.0,5.7,5.3,0.0,1.9,0.0,1.6,6.0,1.5,1.2,3.1,3.0,4.9,13.0,0.0,4.2,0.3,1.3,2.6,2.1,0.0,8.1,7.4,9.5,5.7,2.7,22.1,5.7,20.4,'NaN']})
 
USGS_Kuparuk_Month_Discharge

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Surface-Water Annual Statistics

variable = 'USGS_Kuparuk_annual'

website: https://waterdata.usgs.gov/nwis/annual/?referred_module=sw&site_no=15896000&por_15896000_1204=624324,00010,1204,1978,2020&site_no=15896000&por_15896000_1205=624324,00060,1205,1971,2021&partial_periods=on&year_type=W&format=html_table&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=parameter_selection_list

USGS_Kuparuk_annual = pd.DataFrame({'Year':[1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],
                   'Temperature_water_(C)':['NaN','NaN','NaN','NaN','NaN','NaN','NaN',6.46,11.08,'NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN',9.02,9.13,9.75,9.1,8.39,10.31,7.12,'NaN'],
                   'Discharge_(ft3/s)':[4657,4568,4943,1873,822.6,1428,1484,1390,1008,1590,1412,2304,1048,1431,725.5,1524,1167,1264,2125,849.1,1030,1212,1436,1283,1540,1639,2224,1169,1265,1670,1388,1666,1631,1823,1227,1281,757.5,744.4,1616,1364,1261,1222,2425,2728,2154,2049,1894,3192,2086,1423,1158]})
 
USGS_Kuparuk_annual

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# Adding to Other DataFrames
 
# Creating temporary dataframe to append to big Colville Dataframe
temporary = pd.DataFrame(USGS_Kuparuk_annual)
 
# Changing to numerical 
cols = temporary.columns.drop('Year')
temporary[cols] = temporary[cols].apply(pd.to_numeric, errors='ignore')
 
# Add NaN to cells that don't have data
temporary = temporary.replace(' ',np.NaN)
 
# Adding this Dataframe into the big Kuparuk Dataframe
Kuparuk = Kuparuk.append(temporary)
Kuparuk

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Streamflow Measurements

variable = 'USGS_Kuparuk_discharge'

website = https://waterdata.usgs.gov/nwis/measurements/?site_no=15896000&agency_cd=USGS

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Number of tables on site:  1

# Lets go ahead and scrape first table with HTML code gdp[0]
table1 = gdp[0]
 
# the head will form our column names
body = table1.find_all("tr")
 
# Head values (Column names) are the first items of the body list
head = body[0] # 0th item is the header row
body_rows = body[1:] # All other items becomes the rest of the rows
 
# Lets now iterate through the head HTML code and make list of clean headings
 
# Declare empty list to keep Columns names
headings = []
for item in head.find_all("th"): # loop through all th elements
    # convert the th elements to text and strip "\n"
    item = (item.text).rstrip("\n")
    # append the clean column name to headings
    headings.append(item)
    
print(headings)

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['Meas.Number', '\xa0 Date \xa0 \xa0 \xa0 Time', 'TimeDatum', 'MeasurementUsed?', ' \xa0 Who', 'MeasuringAgency', 'Streamflow(ft3/s)', 'GageHeight(ft)', 'GHChange(ft)', 'Meas.Duration(hr)', 'Meas.Rated', ' \xa0 Control', 'FlowAdjust.Code\xa0']

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['Meas.Number',
 '\xa0 Date \xa0 \xa0 \xa0 Time',
 'TimeDatum',
 'MeasurementUsed?',
 ' \xa0 Who',
 'MeasuringAgency',
 'Streamflow(ft3/s)',
 'GageHeight(ft)',
 'GHChange(ft)',
 'Meas.Duration(hr)',
 'Meas.Rated',
 ' \xa0 Control',
 'FlowAdjust.Code\xa0']

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# Next loop though the rest of the rows
 
#print(body_rows[0])
all_rows = [] # will be a list for list for all rows
for row_num in range(len(body_rows)): # A row at a time
    row = [] # this will old entries for one row
    for row_item in body_rows[row_num].find_all("td"): #loop through all row entries
        # row_item.text removes the tags from the entries
        # the following regex is to remove \xa0 and \n and comma from row_item.text
        # xa0 encodes the flag, \n is the newline and comma separates thousands in numbers
        aa = re.sub("(\xa0)|(\n)|,","",row_item.text)
        #append aa to row - note one row entry is being appended
        row.append(aa)
    # append one row to all_rows
    all_rows.append(row)

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# Reformating/ Cleaning 
 
# Fixing the Date/Time column to become numeric
USGS_Kuparuk_discharge['SampleDatetime'] = pd.to_datetime(USGS_Kuparuk_discharge['SampleDatetime'])
 
# Changing to numerical 
cols = USGS_Kuparuk_discharge.columns.drop('SampleDatetime')
 
USGS_Kuparuk_discharge[cols] = USGS_Kuparuk_discharge[cols].apply(pd.to_numeric, errors='ignore')
USGS_Kuparuk_discharge

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# Adding to Other DataFrames
 
# Creating temporary dataframe to append to big Colville Dataframe
temporary = pd.DataFrame(USGS_Kuparuk_discharge,
                     columns=['SampleDatetime',
                              'Stream_flow(ft3/s)',
                              'Gage_height(ft)',
                              'GH_Change(ft)'])
 
# Changing to numerical 
cols = temporary.columns.drop('SampleDatetime')
temporary[cols] = temporary[cols].apply(pd.to_numeric, errors='ignore')
 
# Add NaN to cells that don't have data
temporary = temporary.replace(' ',np.NaN)
 
temporary

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Monthly Mean Water Temperature (C)

variable = 'USGS_Kuparuk_temperature'
1977-10-01 -> 2020-09-30
in C

website: https://nwis.waterdata.usgs.gov/ak/nwis/dvstat/?referred_module=sw&site_no=15896000&por_15896000_1204=624324,00010,1204,1978-07-27,2020-09-22&site_no=15896000&por_15896000_1205=624324,00060,1205,1971-05-31,2020-10-05&format=html_table&stat_cds=mean_va&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=parameter_selection_list

USGS_Kuparuk_temperature = pd.DataFrame({'Day':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],
                   'Jan':['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN'],
                   'Feb':['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN'],
                   'Mar':['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN'],
                   'Apr':['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN'],
                   'May':['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN'],
                   'Jun':['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN',0.1,0.2,'NaN','NaN','NaN','NaN','NaN','NaN','NaN',1.8,2.8,6.6,'NaN'],
                   'Jul':[4.7,3.9,3.7,3.7,4,3.8,5.7,6.6,6.7,7.5,7.7,7.6,8.2,8.2,8.6,8.7,8.8,9.2,9.3,9.8,11.4,11.7,12.1,12.7,11.8,11.2,10.1,10.2,10.9,11.5,'NaN'],
                   'Aug':[12.5,13.7,14.2,14,13.5,13.7,13.7,12.7,12,11.7,11.3,11.8,13.1,14.7,15.1,13.1,12.7,12.6,11.8,11.1,11,11.3,11.2,11.6,11.5,11.1,11.2,11.4,11.9,12.2,12.2],
                   'Sep':[12.1,11,10.4,9.8,9.4,9.3,10.1,10.1,10.1,9.4,9.1,9.3,8.5,8.5,8.5,8.3,7.9,8.3,8.8,8.9,8.3,8,7.8,8.1,8,7.7,7.3,7.1,6.6,6,5.5],
                   'Oct':[5.2,5.4,5.9,6.3,6.4,5.7,5.7,5.2,5,4.6,3.8,3.6,4.4,5.2,5.2,4.7,4.3,3.8,3.3,2.8,2.1,1.9,2,2,2.3,1.6,'NaN','NaN','NaN','NaN','NaN'],
                   'Nov':['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN'],
                   'Dec':['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN']})
 
USGS_Kuparuk_temperature

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Stream Historical/Current Data (2016)

variable : 'USGS_Kuparuk_hist'

url: https://nwis.waterdata.usgs.gov/nwis/uv?cb_00010=on&cb_00060=on&cb_00065=on&format=html&site_no=15896000&period=&begin_date=2016-07-01&end_date=2021-07-18

## # Getting the HTML 
url = 'https://nwis.waterdata.usgs.gov/nwis/uv?cb_00010=on&cb_00060=on&cb_00065=on&format=html&site_no=15896000&period=&begin_date=2016-07-01&end_date=2021-07-18'
#url = 'https://nwis.waterdata.usgs.gov/nwis/uv?cb_00010=on&cb_00060=on&cb_00065=on&format=rdb&site_no=15896000&period=&begin_date=2016-07-01&end_date=2021-07-18'
# Make a GET request to fetch the raw HTML content
html_content = requests.get(url).text
 
# Parse HTML code for the entire site
soup = BeautifulSoup(html_content, "lxml")
 
#print(soup.prettify()) # print the parsed data of html

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Number of tables on site:  1

# Lets go ahead and scrape first table with HTML code gdp[0]
table1 = gdp[0]
 
# the head will form our column names
body = table1.find_all("tr")
 
# Head values (Column names) are the first items of the body list
head = body[0] # 0th item is the header row
body_rows = body[1:] # All other items becomes the rest of the rows
 
# Lets now iterate through the head HTML code and make list of clean headings
 
# Declare empty list to keep Columns names
headings = []
for item in head.find_all("th"): # loop through all th elements
    # convert the th elements to text and strip "\n"
    item = (item.text).rstrip("\n")
    # append the clean column name to headings
    headings.append(item)
    
print(headings)

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['Date / Time', '   Temper- ature, water, deg C,', '      Dis-charge, ft3/s,', '      Gageheight,  feet,']

# Next loop though the rest of the rows
 
#print(body_rows[0])
all_rows = [] # will be a list for list for all rows
for row_num in range(len(body_rows)): # A row at a time
    row = [] # this will old entries for one row
    for row_item in body_rows[row_num].find_all("td"): #loop through all row entries
        # row_item.text removes the tags from the entries
        # the following regex is to remove \xa0 and \n and comma from row_item.text
        # xa0 encodes the flag, \n is the newline and comma separates thousands in numbers
        aa = re.sub("(\xa0)|(\n)|,","",row_item.text)
        #append aa to row - note one row entry is being appended
        row.append(aa)
    # append one row to all_rows
    all_rows.append(row)

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# Reformating/ Cleaning 
 
# Fixing the Date/Time column to become numeric
USGS_Kuparuk_hist['SampleDatetime'] = pd.to_datetime(USGS_Kuparuk_hist['SampleDatetime'])
 
# Holding onto datetime for later 
cols = USGS_Kuparuk_hist.drop('SampleDatetime',axis =1)
 
cols
 
# Reformating/ Cleaning 
 
# Fixing the Date/Time column to become numeric
USGS_Colville_hist['SampleDatetime'] = pd.to_datetime(USGS_Colville_hist['SampleDatetime'])
 
# Holding onto datetime for later 
cols = USGS_Colville_hist.drop('SampleDatetime',axis =1)
 
cols.columns
 

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/usr/local/lib/python3.7/dist-packages/dateutil/parser/_parser.py:1212: UnknownTimezoneWarning:

tzname AKDT identified but not understood.  Pass `tzinfos` argument in order to correctly return a timezone-aware datetime.  In a future version, this will raise an exception.

/usr/local/lib/python3.7/dist-packages/dateutil/parser/_parser.py:1212: UnknownTimezoneWarning:

tzname AKST identified but not understood.  Pass `tzinfos` argument in order to correctly return a timezone-aware datetime.  In a future version, this will raise an exception.

Index(['Discharge_ft2/s', 'Precipitation_total_in', 'Temperature_air_C',
       'Gageheight_ft', 'Gageheight_ft_2'],
      dtype='object')

# Dropping the stupid character that don't belong/ keep numbers 
 
for i in ['Discharge_ft2/s', 'Precipitation_total_in', 'Temperature_air_C','Gageheight_ft', 'Gageheight_ft_2']:
  cols[i]= cols[i].str.replace('[^0-9.]','')
  #cols[i]= cols[i].str.replace('P','')
 
# Join datetime back into dataframe
cols = cols.join(USGS_Kuparuk_hist['SampleDatetime'])
#cols
 
# Rename USGS_Kuparuk_hist
USGS_Kuparuk_hist = cols
USGS_Kuparuk_hist

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= All Kuparuk

# From Datetime Column, make new year, month, day, time column 
Kuparuk['Datetime'] = pd.DatetimeIndex(Kuparuk['SampleDatetime'])
Kuparuk['year'] = pd.DatetimeIndex(Kuparuk['SampleDatetime']).year
Kuparuk['month'] = pd.DatetimeIndex(Kuparuk['SampleDatetime']).month
Kuparuk['day'] = pd.DatetimeIndex(Kuparuk['SampleDatetime']).day
Kuparuk['time'] = pd.DatetimeIndex(Kuparuk['SampleDatetime']).time
 
Kuparuk.set_index('SampleDatetime',inplace = True)
 
Kuparuk

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3.Sagavanirtok River

USGS: SAGAVANIRKTOK R NR PUMP STA 3 AK

Variables:

Water Quality Samples = 'USGS_Sagavanirtok_water'
Monthly Mean Water Temperature = 'USGS_Sagavanirtok_temperature'
Mean Daily Discharge (ft3/s) = 'USGS_Sagavanirtok_discharge'
Streamflow Measurements = 'USGS_Sagavanirtok_discharge'

Latitude
Drainage area
Gage datum

Water Quality Samples

variable = 'USGS_Sagavanirtok_water'

Latitude 69°00'57", Longitude 148°49'04" NAD83 Drainage area 1,850 square miles Gage datum 1,117.35 feet above NAVD88

website: https://nwis.waterdata.usgs.gov/ak/nwis/qwdata/?site_no=15908000&agency_cd=USGS&inventory_output=0&rdb_inventory_output=file&TZoutput=0&pm_cd_compare=Greater%20than&radio_parm_cds=all_parm_cds&format=html_table&qw_attributes=0&qw_sample_wide=wide&rdb_qw_attributes=0&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=brief_list

# Getting the HTML 
url = 'https://nwis.waterdata.usgs.gov/ak/nwis/qwdata/?site_no=15908000&agency_cd=USGS&inventory_output=0&rdb_inventory_output=file&TZoutput=0&pm_cd_compare=Greater%20than&radio_parm_cds=all_parm_cds&format=html_table&qw_attributes=0&qw_sample_wide=wide&rdb_qw_attributes=0&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=brief_list'
 
# Make a GET request to fetch the raw HTML content
html_content = requests.get(url).text
 
# Parse HTML code for the entire site
soup = BeautifulSoup(html_content, "lxml")
 
#print(soup.prettify()) # print the parsed data of html

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Number of tables on site:  1

# Lets go ahead and scrape first table with HTML code gdp[0]
table1 = gdp[0]
 
# the head will form our column names
body = table1.find_all("tr")
 
# Head values (Column names) are the first items of the body list
head = body[0] # 0th item is the header row
body_rows = body[1:] # All other items becomes the rest of the rows
 
# Lets now iterate through the head HTML code and make list of clean headings
 
# Declare empty list to keep Columns names
headings = []
for item in head.find_all("th"): # loop through all th elements
    # convert the th elements to text and strip "\n"
    item = (item.text).rstrip("\n")
    # append the clean column name to headings
    headings.append(item)
    
print(headings)

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['SampleDatetime', 'Timedatum\xa0', 'Timedatumreliabilitycode\xa0', 'SampleMediumCode\xa0', 'AgencyCollectingSample,Code\xa0', '     Stream width,  feet(00004)', ' Loca-tion inX-sect.lookingdwnstrmft from l bank(00009)', '   Temper- ature, water, deg C(00010)', '   Temper- ature,  air, deg C(00020)', '  Instan-taneous  dis-charge, ft3/s(00061)', '      Gageheight,  feet(00065)', '    Gage height above datum meters(30207)', '    Dis-charge,instan-taneous  m3/s(30209)', '     Samplepurpose  code(71999)', '     Sam- plingmethod,  code(82398)']

# Next loop though the rest of the rows
 
#print(body_rows[0])
all_rows = [] # will be a list for list for all rows
for row_num in range(len(body_rows)): # A row at a time
    row = [] # this will old entries for one row
    for row_item in body_rows[row_num].find_all("td"): #loop through all row entries
        # row_item.text removes the tags from the entries
        # the following regex is to remove \xa0 and \n and comma from row_item.text
        # xa0 encodes the flag, \n is the newline and comma separates thousands in numbers
        aa = re.sub("(\xa0)|(\n)|,","",row_item.text)
        #append aa to row - note one row entry is being appended
        row.append(aa)
    # append one row to all_rows
    all_rows.append(row)

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# Renaming Columns ( bc they were gross before)
 
headings = ['SampleDatetime',
 'Time datum',
 'Time datum reliability code',
 'Sample Medium Code',
 'Agency Collecting Sample, Code',
 'Stream width (ft)',
 'Location intersect (lookingdwnstream) from l bank)',
 'Temperature,water(C)(00010)',
 'Temperature,air(C)(00020)',
 'Instantaneous_discharge(ft3/s)(00061)',
 'Gageheight_feet(00065)',
 'Gage_height_meters(30207)',
 'Discharge_instantaneous(m3/s)(30209)',
 'Sample purpose code(71999)',
 'Sampling method code(82398)']
 

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# Reformating/ Cleaning 
 
# Fixing the Date/Time column to become numeric
USGS_Sagavanirtok_water['SampleDatetime'] = pd.to_datetime(USGS_Sagavanirtok_water['SampleDatetime'])
#USGS_Sagavanirtok_water = USGS_Sagavanirtok_water.apply(pd.to_numeric)
 
# Fixing numerical columns
USGS_Sagavanirtok_water['Temperature,water(C)(00010)'] = pd.to_numeric(USGS_Sagavanirtok_water['Temperature,water(C)(00010)'])
 
# Changing to numerical 
cols = USGS_Sagavanirtok_water.columns.drop('SampleDatetime')
 
USGS_Sagavanirtok_water[cols] = USGS_Sagavanirtok_water[cols].apply(pd.to_numeric, errors='ignore')
USGS_Sagavanirtok_water

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SampleDatetime                                        datetime64[ns]
Time datum                                                    object
Time datum reliability code                                   object
Sample Medium Code                                            object
Agency Collecting Sample, Code                                object
Stream width (ft)                                              int64
Location intersect (lookingdwnstream) from l bank)           float64
Temperature,water(C)(00010)                                  float64
Temperature,air(C)(00020)                                    float64
Instantaneous_discharge(ft3/s)(00061)                          int64
Gageheight_feet(00065)                                       float64
Gage_height_meters(30207)                                    float64
Discharge_instantaneous(m3/s)(30209)                           int64
Sample purpose code(71999)                                   float64
Sampling method code(82398)                                    int64
dtype: object

# Adding to Other DataFrames
 
# Adding this Dataframe into the big Sagavanirtok Dataframe
Sagavanirtok = pd.DataFrame(USGS_Sagavanirtok_water,columns=['SampleDatetime','Stream width (ft)','Temperature,water(C)(00010)',
 'Temperature,air(C)(00020)',
 'Instantaneous_discharge(ft3/s)(00061)',
 'Gageheight_feet(00065)',
 'Gage_height_meters(30207)',
 'Discharge_instantaneous(m3/s)(30209)',])

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Mean Daily Discharge (ft3/s)

variable = 'USGS_Sagavanirtok_discharge'
Calculation Period 1981-10-01 -> 2021-09-30

website: https://nwis.waterdata.usgs.gov/ak/nwis/dvstat/?referred_module=sw&site_no=15908000&por_15908000_1214=624330,00010,1214,2014-06-10,2020-09-21&site_no=15908000&por_15908000_1211=624330,00060,1211,1982-09-01,2020-10-07&format=html_table&stat_cds=mean_va&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=parameter_selection_list

USGS_Sagavanirtok_discharge = pd.DataFrame({'Day':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],
                   'Jan':[110,109,108,106,105,103,102,101,99,98,97,96,94,94,92,91,90,89,88,88,86,85,84,83,82,82,80,80,78,77,76],
                   'Feb':[75,75,75,74,73,73,73,72,72,71,70,70,70,69,68,68,68,67,67,66,66,66,66,66,66,65,65,65,59,'NaN','NaN'],
                   'Mar':[65,65,65,65,64,64,63,63,63,63,63,62,62,62,62,62,62,62,61,61,61,61,61,61,60,60,60,60,60,61,61],
                   'Apr':[60,60,60,61,60,60,61,61,61,61,61,61,60,60,61,61,61,62,62,62,62,62,63,64,65,69,71,73,76,82,'NaN'],
                   'May':[93,92,91,95,105,127,152,177,220,293,436,521,543,566,597,684,834,1130,1490,1860,1920,2160,2500,3140,3870,4280,4460,4450,4630,4670,4980],
                   'Jun':[5320,5360,5200,5340,5520,5920,6290,6450,6600,6650,6460,6370,5930,6060,6310,6300,6560,6330,6280,6100,6140,6060,6050,5880,5580,5520,5690,5290,5250,5170,'NaN'],
                   'Jul':[5440,5540,5980,5530,5270,5460,5490,5290,4920,5080,5250,5020,4780,4920,5040,4490,4680,5460,5250,5130,5470,5230,4760,4630,4450,4230,4250,4520,4360,4440,4430],
                   'Aug':[4510,4490,4770,5250,4800,4460,4570,4500,4920,4590,4180,4080,4060,4530,4920,5330,4740,4680,4350,4070,3770,3660,3510,3340,3160,3460,4160,3780,3310,3080,2890],
                   'Sep':[3020,3090,2870,3210,3070,2880,2830,2670,2490,2310,2220,2190,2420,2190,2030,1910,1870,1820,1710,1640,1600,1530,1470,1400,1330,1270,1230,1200,1150,1090,'NaN'],
                   'Oct':[1060,1100,1040,991,951,903,848,791,771,736,713,697,683,653,625,601,584,571,555,539,526,508,497,485,473,459,448,435,421,411,401],
                   'Nov':[388,380,370,361,353,343,334,328,320,312,306,299,291,282,275,269,263,257,253,245,239,234,228,223,217,212,208,203,198,195,'NaN'],
                   'Dec':[190,186,183,178,174,172,168,164,161,158,154,152,150,147,144,141,139,137,134,133,130,128,125,124,121,120,117,116,114,112,111]})
 
USGS_Sagavanirtok_discharge

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Monthly Average Discharge (ft3/s)

variable: 'USGS_Sagavanirtok_Month_Discharge'

Calculation Period 1983-01-01 -> 2020-09-30

website: https://nwis.waterdata.usgs.gov/ak/nwis/monthly/?referred_module=sw&site_no=15908000&por_15908000_1211=624330,00060,1211,1982-09,2020-10&format=html_table&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=parameter_selection_list

USGS_Sagavanirtok_Month_Discharge = pd.DataFrame({'Year':[1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020],
                   'Jan':[0,30,5.42,18.4,16.9,28.7,4.71,6.39,0,0,10.3,17.3,10.2,72.7,62.7,180.3,91.7,7.16,122.3,169.7,153.2,45.3,204.8,60.6,35.3,65.2,50.1,82.3,86,108.5,50,235.5,257.1,194.5,171,323.8,244.7,275],
                   'Feb':[0,3.45,0,5.75,0.714,3.45,0.357,0,0,0,0,0.536,0,52.4,50,149.6,57.1,1.31,103.5,153.6,110.8,7.55,185,14.6,23.9,50,30.8,54.1,68,61.9,26.7,184.3,219.5,160,156.4,195.8,231,275],
                   'Mar':[0,0,0,1.06,0,0,0,0,0,0,0,0,0,43.6,44.4,127.7,38,1,91.4,143.2,83.8,1,168.7,10,15,42.6,20.5,46.6,60,55,18.4,159.8,213.9,164.2,141.6,154.9,239.5,275],
                   'Apr':[50.1,0,0.1,0,0,0,0,6.2,0,0,0,1.67,4.73,40.5,43,116.7,26.1,1,83.5,136,67,1,159,10,15,40,38.6,41.8,75.5,66,15.3,146.7,220,191.3,121.3,135.2,279.8,275],
                   'May':[477.9,107.4,524.6,4.77,1437,1696,113.1,2097,768.1,107.6,3588,750.3,3549,2177,1583,2590,2278,40.6,78.6,1308,71.4,2454,1711,1246,561.8,143.9,2580,2585,3735,946.5,387.4,1905,6163,2518,2493,2196,3112,2649],
                   'Jun':[3897,4878,3875,5489,4431,4392,6973,4148,7282,9737,6209,4026,6975,7704,6524,5966,5624,8811,5566,3304,7911,5722,3735,5070,4160,3818,9543,4792,3296,5912,10580,8987,4586,5757,5171,8376,6632,5562],
                   'Jul':[3777,3633,4469,4482,4972,3135,4425,3003,2839,5470,6188,6676,7370,4372,5549,4151,6655,4820,5091,3926,7348,6166,3064,5015,2873,4743,5149,5620,3741,5672,6486,7200,3027,5489,4981,8670,6303,3225],
                   'Aug':[2380,5150,2299,3031,6252,3572,4720,1897,2810,4887,3373,5157,3901,2285,5095,4554,5070,3590,4015,4635,6355,5247,1464,4087,3195,2744,2699,6883,2505,4043,3897,4644,3775,3983,5040,7096,9754,3154],
                   'Sep':[882.8,918.6,1371,1888,2535,1907,2316,1439,1267,1328,2549,2259,1977,1956,3984,2043,2782,1213,1477,3032,2000,1348,965.9,1617,990.3,678.9,1685,1702,1515,2508,1585,2593,2139,2364,3445,7457,3384,1781],
                   'Oct':[289.7,308.4,279.8,406.5,414,379.4,826.5,599.7,606.1,550.3,839.4,643.9,1172,588.7,815.8,775.8,669.4,457.4,507.7,910.3,1372,674.5,560,731.6,426.1,338,658.4,551.6,552.6,687.8,708.5,1015,566.8,789.4,899.2,1214,952,'NaN'],
                   'Nov':[76,140,92.8,130.5,122,120.7,254,140.1,217.3,204.7,350.3,196.6,358.3,273.7,350.7,333.3,269.7,232.7,259.7,401.7,373.3,350.7,229.3,296,201,208.7,323,376.7,303,368,356.3,495.3,282.3,242.7,495,695.4,409.5,'NaN'],
                   'Dec':[44.8,50,43.2,48.1,68.7,26.6,65,4.03,37,41.6,84,51.3,132.7,104.6,232.9,167.4,69.5,154.8,198.1,220,137.3,241.9,151.6,88.2,108.7,98.2,177.7,160.5,169,163,300.3,307.4,233.5,193.9,425.4,389.6,286.6,'NaN']})
 
USGS_Sagavanirtok_Month_Discharge

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Surface-Water Annual Statistics

variable = 'USGS_Sagavanirtok_annual'

website: https://nwis.waterdata.usgs.gov/ak/nwis/annual/?referred_module=sw&site_no=15908000&por_15908000_1214=624330,00010,1214,2014,2020&site_no=15908000&por_15908000_1211=624330,00060,1211,1982,2021&partial_periods=on&year_type=W&format=html_table&date_format=YYYY-MM-DD&rdb_compression=file&submitted_form=parameter_selection_list

USGS_Sagavanirtok_annual = pd.DataFrame({'Year':[1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],
                   'Temperature_water_(C)':['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN',9.23,9.47,8.77,10.04,7.58,10.18,8.92,'NaN'],
                   'Discharge_(ft3/s)':[1347,993.3,1266,1093,1282,1699,1282,1595,1151,1311,1866,1905,1694,2071,1697,2000,1783,2005,1623,1463,1490,2148,1917,1082,1516,1088,1093,1874,1931,1358,1703,2026,2294,1882,1831,1923,3047,2727,1596,1317]})
 
USGS_Sagavanirtok_annual

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Streamflow Measurements

variable = 'USGS_Sagavanirtok_discharge'

website: https://nwis.waterdata.usgs.gov/ak/nwis/measurements/?site_no=15908000&agency_cd=USGS

# Getting the HTML 
url = 'https://nwis.waterdata.usgs.gov/ak/nwis/measurements/?site_no=15908000&agency_cd=USGS'
 
# Make a GET request to fetch the raw HTML content
html_content = requests.get(url).text
 
# Parse HTML code for the entire site
soup = BeautifulSoup(html_content, "lxml")
 
# On site there are 1 tables with the class "tablesorter"
# The following line will generate a list of HTML content for each table
 
# class = "tablesorter dataList tablesorter
 
gdp = soup.find_all("table", attrs={"class": "tablesorter"})
print("Number of tables on site: ",len(gdp))

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Number of tables on site:  1

# Lets go ahead and scrape first table with HTML code gdp[0]
table1 = gdp[0]
 
# the head will form our column names
body = table1.find_all("tr")
 
# Head values (Column names) are the first items of the body list
head = body[0] # 0th item is the header row
body_rows = body[1:] # All other items becomes the rest of the rows
 
# Lets now iterate through the head HTML code and make list of clean headings
 
# Declare empty list to keep Columns names
headings = []
for item in head.find_all("th"): # loop through all th elements
    # convert the th elements to text and strip "\n"
    item = (item.text).rstrip("\n")
    # append the clean column name to headings
    headings.append(item)
    
print(headings)

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['Meas.Number', '\xa0 Date \xa0 \xa0 \xa0 Time', 'TimeDatum', 'MeasurementUsed?', ' \xa0 Who', 'MeasuringAgency', 'Streamflow(ft3/s)', 'GageHeight(ft)', 'GHChange(ft)', 'Meas.Duration(hr)', 'Meas.Rated', ' \xa0 Control', 'FlowAdjust.Code\xa0']

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['Meas.Number',
 '\xa0 Date \xa0 \xa0 \xa0 Time',
 'TimeDatum',
 'MeasurementUsed?',
 ' \xa0 Who',
 'MeasuringAgency',
 'Streamflow(ft3/s)',
 'GageHeight(ft)',
 'GHChange(ft)',
 'Meas.Duration(hr)',
 'Meas.Rated',
 ' \xa0 Control',
 'FlowAdjust.Code\xa0']

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# Next loop though the rest of the rows
 
#print(body_rows[0])
all_rows = [] # will be a list for list for all rows
for row_num in range(len(body_rows)): # A row at a time
    row = [] # this will old entries for one row
    for row_item in body_rows[row_num].find_all("td"): #loop through all row entries
        # row_item.text removaes the tags from the entries
        # the following regex is to remove \xa0 and \n and comma from row_item.text
        # xa0 encodes the flag, \n is the newline and comma separates thousands in numbers
        aa = re.sub("(\xa0)|(\n)|,","",row_item.text)
        #append aa to row - note one row entry is being appended
        row.append(aa)
    # append one row to all_rows
    all_rows.append(row)

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# Reformating/ Cleaning 
 
# Fixing the Date/Time column to become numeric
USGS_Sagavanirtok_discharge['SampleDatetime'] = pd.to_datetime(USGS_Sagavanirtok_discharge['SampleDatetime'])
 
# Changing to numerical 
cols = USGS_Sagavanirtok_discharge.columns.drop('SampleDatetime')
 
USGS_Sagavanirtok_discharge[cols] = USGS_Sagavanirtok_discharge[cols].apply(pd.to_numeric, errors='ignore')
USGS_Sagavanirtok_discharge

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Stream Historical/Current Data (2016)

variable : 'USGS_Sagavanirtok_hist'

website: https://nwis.waterdata.usgs.gov/ak/nwis/uv?cb_00010=on&cb_00060=on&cb_00065=on&format=html&site_no=15908000&period=&begin_date=2016-07-01&end_date=2021-07-19

# Getting the HTML 
url = 'https://nwis.waterdata.usgs.gov/ak/nwis/uv?cb_00010=on&cb_00060=on&cb_00065=on&format=html&site_no=15908000&period=&begin_date=2016-07-01&end_date=2021-07-19'
 
# Make a GET request to fetch the raw HTML content
html_content = requests.get(url).text
 
# Parse HTML code for the entire site
soup = BeautifulSoup(html_content, "lxml")
 
# On site there are 1 tables with the class "tablesorter"
# The following line will generate a list of HTML content for each table
 
# class = "tablesorter dataList tablesorter
 
gdp = soup.find_all("table", attrs={"class": "tablesorter"})
print("Number of tables on site: ",len(gdp))

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Number of tables on site:  1

# Lets go ahead and scrape first table with HTML code gdp[0]
table1 = gdp[0]
 
# the head will form our column names
body = table1.find_all("tr")
 
# Head values (Column names) are the first items of the body list
head = body[0] # 0th item is the header row
body_rows = body[1:] # All other items becomes the rest of the rows
 
# Lets now iterate through the head HTML code and make list of clean headings
 
# Declare empty list to keep Columns names
headings = []
for item in head.find_all("th"): # loop through all th elements
    # convert the th elements to text and strip "\n"
    item = (item.text).rstrip("\n")
    # append the clean column name to headings
    headings.append(item)
    
print(headings)

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['Date / Time', '      Dis-charge, ft3/s,', '      Gageheight,  feet,', '   Temper- ature, water, deg C,']

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# Next loop though the rest of the rows
 
#print(body_rows[0])
all_rows = [] # will be a list for list for all rows
for row_num in range(len(body_rows)): # A row at a time
    row = [] # this will old entries for one row
    for row_item in body_rows[row_num].find_all("td"): #loop through all row entries
        # row_item.text removaes the tags from the entries
        # the following regex is to remove \xa0 and \n and comma from row_item.text
        # xa0 encodes the flag, \n is the newline and comma separates thousands in numbers
        aa = re.sub("(\xa0)|(\n)|,","",row_item.text)
        #append aa to row - note one row entry is being appended
        row.append(aa)
    # append one row to all_rows
    all_rows.append(row)

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# Reformating/ Cleaning 
 
# Fixing the Date/Time column to become numeric
USGS_Sagavanirtok_hist['SampleDatetime'] = pd.to_datetime(USGS_Sagavanirtok_hist['SampleDatetime'])
 
# Holding onto datetime for later 
cols = USGS_Sagavanirtok_hist.drop('SampleDatetime',axis =1)
 
cols
 
# Reformating/ Cleaning 
 
# Fixing the Date/Time column to become numeric
USGS_Sagavanirtok_hist['SampleDatetime'] = pd.to_datetime(USGS_Sagavanirtok_hist['SampleDatetime'])
 
# Holding onto datetime for later 
cols = USGS_Sagavanirtok_hist.drop('SampleDatetime',axis =1)
 
cols.columns
 

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/usr/local/lib/python3.7/dist-packages/dateutil/parser/_parser.py:1212: UnknownTimezoneWarning:

tzname AKDT identified but not understood.  Pass `tzinfos` argument in order to correctly return a timezone-aware datetime.  In a future version, this will raise an exception.

/usr/local/lib/python3.7/dist-packages/dateutil/parser/_parser.py:1212: UnknownTimezoneWarning:

tzname AKST identified but not understood.  Pass `tzinfos` argument in order to correctly return a timezone-aware datetime.  In a future version, this will raise an exception.

Index(['Discharge_ft2/s', 'Gageheight_ft', 'Temperature_water_C'], dtype='object')

# Dropping the stupid character that don't belong/ keep numbers 
 
for i in ['Discharge_ft2/s',
            'Gageheight_ft',
            'Temperature_water_C']:
            
  cols[i]= cols[i].str.replace('[^0-9.]','')
  #cols[i]= cols[i].str.replace('P','')
 
# Join datetime back into dataframe
cols = cols.join(USGS_Sagavanirtok_hist['SampleDatetime'])
 
# Rename USGS_Kuparuk_hist
USGS_Sagavanirtok_hist = cols
USGS_Sagavanirtok_hist
 

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# Changing to numerical 
cols = USGS_Sagavanirtok_hist.columns.drop('SampleDatetime')
USGS_Sagavanirtok_hist[cols] = USGS_Sagavanirtok_hist[cols].apply(pd.to_numeric, errors='ignore')
 
# Add NaN to cells that don't have data
USGS_Sagavanirtok_hist = USGS_Sagavanirtok_hist.replace(' ',np.NaN)
 
# Adding this Dataframe into the big Kuparuk Dataframe
Sagavanirtok = Sagavanirtok.append(USGS_Sagavanirtok_hist)

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SampleDatetime                           datetime64[ns]
Stream width (ft)                               float64
Temperature,water(C)(00010)                     float64
Temperature,air(C)(00020)                       float64
Instantaneous_discharge(ft3/s)(00061)           float64
Gageheight_feet(00065)                          float64
Gage_height_meters(30207)                       float64
Discharge_instantaneous(m3/s)(30209)            float64
Discharge_ft2/s                                 float64
Gageheight_ft                                   float64
Temperature_water_C                             float64
dtype: object

= All Sagavanirtok

# From Datetime Column, make new year, month, day, time column 
Sagavanirtok['Datetime'] = pd.DatetimeIndex(Sagavanirtok['SampleDatetime'])
Sagavanirtok['year'] = pd.DatetimeIndex(Sagavanirtok['SampleDatetime']).year
Sagavanirtok['month'] = pd.DatetimeIndex(Sagavanirtok['SampleDatetime']).month
Sagavanirtok['day'] = pd.DatetimeIndex(Sagavanirtok['SampleDatetime']).day
Sagavanirtok['time'] = pd.DatetimeIndex(Sagavanirtok['SampleDatetime']).time
 
Sagavanirtok.set_index('SampleDatetime',inplace = True)
 
Sagavanirtok

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PLOTTING

Mapping locations

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# Mapping Data Points
# website for icons: https://fontawesome.com/v5.15/icons?d=gallery&p=9
# NOTE: Maybe for water samples - icon: 'map-marker'
 
# Mapping North Slope Area
North_Slope_map = folium.Map(location = [70.25, -148], zoom_start=7)
 
# Colville River 
  # USGS: COLVILLE R AT UMIAT Data - icon: info-sign
folium.Marker(
    location=[Data_locations['USGS_Kuparuk']['Latitude'], Data_locations['USGS_Kuparuk']['Longitude']],
    popup="USGS: COLVILLE R AT UMIAT Data ",
    icon=folium.Icon(color="blue", icon="info-sign"),
).add_to(North_Slope_map)
 
# Kuparuk River
  # USGS: KUPARUK R NR DEADHORSE AK Data - icon: info-sign
folium.Marker(
    location=[Data_locations['USGS_Colville']['Latitude'], Data_locations['USGS_Colville']['Longitude']],
    popup="USGS: KUPARUK R NR DEADHORSE AK Data",
    icon=folium.Icon(color="orange", icon="info-sign"),
).add_to(North_Slope_map)
 
# Sagavanirtok River
  # USGS: SAGAVANIRKTOK R NR PUMP STA 3 AK Data - icon: info-sign
folium.Marker(
    location=[Data_locations['USGS_Sagavanirtok']['Latitude'], Data_locations['USGS_Sagavanirtok']['Longitude']],
    popup="USGS: SAGAVANIRKTOK R NR PUMP STA 3 AK Data",
    icon=folium.Icon(color="green", icon="info-sign"),
).add_to(North_Slope_map)
 
 
 
 
 
# Print map
North_Slope_map

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Plots

All

# Plot: Instantaneous_discharge
 
# Colville
plt.scatter(Colville['Datetime'],Colville['Instantaneous_discharge(ft3/s)(00061)'],color = 'blue', label = 'Colville')
# Kuparuk
plt.scatter(Kuparuk['Datetime'],Kuparuk['Instantaneous_discharge(ft3/s)(00061)'],color = 'orange', label = 'Kuparuk')
# Sagavanirtok
plt.scatter(Sagavanirtok['Datetime'],Sagavanirtok['Instantaneous_discharge(ft3/s)(00061)'],color = 'green', label = 'Sagavanirtok')
 
plt.legend()
plt.xlabel('Years')
plt.ylabel('Instantaneous discharge (ft3/s)')
plt.title('River Instantaneous discharge')
plt.show()

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# Plot: Air Temperature 
 
# Colville
plt.scatter(Colville['Datetime'],Colville['Temperature,air(C)(00020)'],color = 'blue', label = 'Colville')
# Kuparuk
plt.scatter(Kuparuk['Datetime'],Kuparuk['Temperature,air(C)(00020)'],color = 'orange', label = 'Kuparuk')
# Sagavanirtok
plt.scatter(Sagavanirtok['Datetime'],Sagavanirtok['Temperature,air(C)(00020)'],color = 'green', label = 'Sagavanirtok')
 
plt.legend()
plt.xlabel('Years')
plt.ylabel('Temperature,air(C)(00020)(ft3/s)')
plt.title('Air Temperature near Rivers')
plt.show()

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# Plot: Temperature Water
 
# Colville
plt.scatter(Colville['Datetime'],Colville['Temperature,water(C)(00010)'], color = 'blue', label = 'Colville')
# Kuparuk
plt.scatter(Kuparuk['Datetime'],Kuparuk['Temperature,water(C)(00010)'],color = 'orange', label = 'Kuparuk')
# Sagavanirtok
plt.scatter(Sagavanirtok['Datetime'],Sagavanirtok['Temperature,water(C)(00010)'],color = 'green', label = 'Sagavanirtok')
 
plt.legend()
plt.xlabel('Years')
plt.ylabel('Temperature (C)')
plt.title('River Water Temperature (C)')
plt.show()
 

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# Plot: Stream width(ft)
 
# Colville
plt.plot(Colville['Datetime'],Colville['Stream width(ft)'], color = 'blue', label = 'Colville')
# Kuparuk
plt.plot(Kuparuk['Datetime'],Kuparuk['Stream width (ft)'],color = 'orange', label = 'Kuparuk')
# Sagavanirtok
plt.plot(Sagavanirtok['Datetime'],Sagavanirtok['Stream width (ft)'],color = 'green', label = 'Sagavanirtok')
 
plt.legend()
plt.xlabel('Years')
plt.ylabel('Stream width(ft)')
plt.title('Stream width(ft) for the Rivers')
plt.show()
 

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# Plot all discharge 
 
plt.scatter(Colville['Datetime'],Colville['Discharge_ft2/s'],color = 'blue', label = 'Colville')
plt.scatter(Kuparuk['Datetime'],Kuparuk['Discharge_ft2/s'],color = 'orange', label = 'Kuparuk')
plt.scatter(Sagavanirtok['Datetime'],Sagavanirtok['Discharge_ft2/s'],color = 'green', label = 'Sagavanirtok', linewidth=1)
 
plt.legend()
plt.xlabel('Years')
plt.ylabel('Stream Discharge (ft2/s)')
plt.title('Stream Discharge (ft2/s) for the Rivers')
plt.rcParams['figure.figsize'] = [10, 8]
plt.xticks(rotation=45)
plt.show()

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# Plot all discharge 2020-2021
 
plt.scatter(Colville.loc['2020':'2021']['Datetime'],Colville.loc['2020':'2021']['Discharge_ft2/s'],color = 'blue', label = 'Colville')
plt.scatter(Kuparuk.loc['2020':'2021']['Datetime'],Kuparuk.loc['2020':'2021']['Discharge_ft2/s'],color = 'orange', label = 'Kuparuk')
plt.scatter(Sagavanirtok.loc['2020':'2021']['Datetime'],Sagavanirtok.loc['2020':'2021']['Discharge_ft2/s'],color = 'green', label = 'Sagavanirtok', linewidth=1)
 
plt.legend()
plt.xlabel('Years')
plt.ylabel('Stream Discharge (ft2/s)')
plt.title('Stream Discharge (ft2/s) for the Rivers')
plt.rcParams['figure.figsize'] = [10, 8]
plt.xticks(rotation=45)
plt.show()

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# Plot All Precipitation
 
plt.scatter(Colville.loc['2021-1':'2021-08']['Datetime'],Colville.loc['2021-1':'2021-08']['Precipitation_total_in'],color = 'blue', label = 'Colville')
plt.scatter(Kuparuk.loc['2021-1':'2021-08']['Datetime'],Kuparuk.loc['2021-1':'2021-08']['Precipitation_total_in'],color = 'orange', label = 'Kuparuk')
 
plt.legend()
plt.xlabel('Years')
plt.ylabel('Total Precipitation (in)')
plt.title('Total Precipitation (in) for the Rivers')
plt.rcParams['figure.figsize'] = [10, 8]
plt.xticks(rotation=45)
plt.show()

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# Plot All Precipitation
 
plt.scatter(Colville.loc['2021-5-15':'2021-08']['Datetime'],Colville.loc['2021-5-15':'2021-08']['Precipitation_total_in'],color = 'blue', label = 'Colville')
plt.scatter(Kuparuk.loc['2021-5-15':'2021-08']['Datetime'],Kuparuk.loc['2021-5-15':'2021-08']['Precipitation_total_in'],color = 'orange', label = 'Kuparuk')
 
plt.legend()
plt.xlabel('Time')
plt.ylabel('Total Precipitation (in)')
plt.title('Total Precipitation (in) for the Rivers')
plt.rcParams['figure.figsize'] = [10, 8]
plt.xticks(rotation=45)
plt.show()

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# Plot All Air Temperature 
 
plt.scatter(Colville['Datetime'],Colville['Temperature_air_C'],color = 'blue', label = 'Colville')
plt.scatter(Kuparuk['Datetime'],Kuparuk['Temperature_air_C'],color = 'orange', label = 'Kuparuk')
 
plt.legend()
plt.xlabel('Years')
plt.ylabel('Air Temperature (C)')
plt.title('Air Temperature (C) for the Rivers')
plt.rcParams['figure.figsize'] = [10, 8]
plt.xticks(rotation=45)
plt.show()

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# Plot All Gage height
 
plt.scatter(Colville['Datetime'],Colville['Gageheight_ft'],color = 'blue', label = 'Colville')
plt.scatter(Kuparuk['Datetime'],Kuparuk['Gageheight_ft'],color = 'orange', label = 'Kuparuk')
plt.scatter(Sagavanirtok['Datetime'],Sagavanirtok['Gageheight_ft'],color = 'green', label = 'Sagavanirtok', linewidth=1)
 
plt.legend()
plt.xlabel('Years')
plt.ylabel('Gageheight (ft)')
plt.title('Gageheight (ft) for the Rivers')
plt.rcParams['figure.figsize'] = [10, 8]
plt.xticks(rotation=45)
plt.show()

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# Plot All Gage height 2020-2021
 
plt.scatter(Colville.loc['2020':'2021']['Datetime'],Colville.loc['2020':'2021']['Gageheight_ft'],color = 'blue', label = 'Colville')
plt.scatter(Kuparuk.loc['2020':'2021']['Datetime'],Kuparuk.loc['2020':'2021']['Gageheight_ft'],color = 'orange', label = 'Kuparuk')
plt.scatter(Sagavanirtok.loc['2020':'2021']['Datetime'],Sagavanirtok.loc['2020':'2021']['Gageheight_ft'],color = 'green', label = 'Sagavanirtok', linewidth=1)
 
plt.legend()
plt.xlabel('Time')
plt.ylabel('Gageheight (ft)')
plt.title('Gageheight (ft) for the Rivers from 2020-2021')
plt.rcParams['figure.figsize'] = [10, 8]
plt.xticks(rotation=45)
plt.show()

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Colville

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Discharge

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# Plot: Discharge Colville
plt.scatter(Colville.loc['2016']['month'],Colville.loc['2016']['Discharge_ft2/s'],color = 'red', label = '2016')
plt.scatter(Colville.loc['2017']['month'],Colville.loc['2017']['Discharge_ft2/s'],color = 'orange', label = '2017')
plt.scatter(Colville.loc['2018']['month'],Colville.loc['2018']['Discharge_ft2/s'],color = 'yellow', label = '2018')
plt.scatter(Colville.loc['2019']['month'],Colville.loc['2019']['Discharge_ft2/s'],color = 'green', label = '2019')
plt.scatter(Colville.loc['2020']['month'],Colville.loc['2020']['Discharge_ft2/s'],color = 'blue', label = '2020')
plt.scatter(Colville.loc['2021']['month'],Colville.loc['2021']['Discharge_ft2/s'],color = 'purple', label = '2021')
 
plt.legend()
plt.xlabel('Months')
plt.ylabel('Discharge ft2/s')
plt.title('Discharge of the Colville River monthly 2016-2021')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.xticks(range(1,13))
 
plt.show()
 

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Precipitation

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Air Temperature

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Gageheight

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# Plot: Gageheight Colville
plt.scatter(Colville.loc['2016']['month'],Colville.loc['2016']['Gageheight_ft'],color = 'red', label = '2016')
plt.scatter(Colville.loc['2017']['month'],Colville.loc['2017']['Gageheight_ft'],color = 'orange', label = '2017')
plt.scatter(Colville.loc['2018']['month'],Colville.loc['2018']['Gageheight_ft'],color = 'yellow', label = '2018')
plt.scatter(Colville.loc['2019']['month'],Colville.loc['2019']['Gageheight_ft'],color = 'green', label = '2019')
plt.scatter(Colville.loc['2020']['month'],Colville.loc['2020']['Gageheight_ft'],color = 'blue', label = '2020')
plt.scatter(Colville.loc['2021']['month'],Colville.loc['2021']['Gageheight_ft'],color = 'purple', label = '2021')
 
plt.legend()
plt.xlabel('Months')
plt.ylabel('Gageheight (ft)')
plt.title('Gageheight (ft) of the Colville River monthly 2016-2021')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.xticks(range(1,13))
plt.show()
 

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Color, Nitrate, Organic carbon

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Index(['Stream width(ft)', 'Temperature,water(C)(00010)',
       'Temperature,air(C)(00020)', 'Instantaneous_discharge(ft3/s)(00061)',
       'Gageheight_feet(00065)', 'Color, water, fltrd, Pt-Co units(00080)',
       'Specific_conductance,wat unfuS/cm @25 degC(00095)',
       'Hydrogen_ion,water,unfltrd calcd,mg/L(00191)',
       'Dissolved_oxygen,(mg/L)(00300)',
       'Dis- solvedoxygen,percentof sat-uration(00301)',
       'pH, water,unfltrd field,  std units(00400)',
       'Carbondioxide water,unfltrd  mg/L(00405)',
       'Bicarbonate,wat unf fixedend pt, field,  mg/L(00440)',
       'Carbonate,wat unf fixedend pt, field,  mg/L(00445)',
       'Nitrate water, fltrd,  mg/L  as N(00618)',
       'Nitrate+nitrite water, fltrd, mg/L  as N(00631)',
       'Ortho-phosphate, water, fltrd, mg/L(00660)',
       'Ortho-phosphate, water, fltrd, mg/L  as P(00671)',
       'Organic_carbon, water,unfltrd  mg/L(00680)',
       'Hardness, water,mg/L as CaCO3(00900)',
       'Noncarb hardness,wat unf field,mg/L as CaCO3(00902)',
       'Calcium water, fltrd,  mg/L(00915)',
       'Magnesium, water, fltrd,  mg/L(00925)',
       'Sodium, water, fltrd,  mg/L(00930)', 'Sodium_adsorption ratio(00931)',
       'Sodium fraction of cations percent(00932)',
       'Potassium, water, fltrd,  mg/L(00935)',
       'Chloride, water, fltrd,  mg/L(00940)',
       'Sulfate water, fltrd,  mg/L(00945)',
       'Fluoride, water, fltrd,  mg/L(00950)',
       'Silica, water, fltrd,mg/L as  SiO2(00955)',
       'Iron, water, fltrd,  ug/L(01046)',
       'Manga- nese, water, fltrd, ug/L(01056)', 'Gage_height_meters(30207)',
       'Discharge_instantaneous(m3/s)(30209)',
       'Fecal coli-form,M-FC0.7u MF cfu/ 100 mL(31625)',
       'Fecal streptococci KF MF, cfu/ 100 mL(31673)',
       'Dissolved_solids_dried @180degCwat flt mg/L(70300)',
       'Dissolved_solids, sum of constituents, mg/L(70301)',
       'Dissolved_solids, water, ton/d(70302)',
       'Dissolved_solids, water, tons/acre-ft(70303)',
       'Nitrate water, fltrd,  mg/L(71851)',
       'Iron, water,unfltrd  ug/L(71885)', 'Stream_flow(ft3/s)',
       'Gage_height(ft)', 'GH_Change(ft)', 'Discharge_ft2/s',
       'Precipitation_total_in', 'Temperature_air_C', 'Gageheight_ft',
       'Gageheight_ft_2', 'Datetime', 'year', 'month', 'day', 'time'],
      dtype='object')

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SampleDatetime
1953-07-28 15:30:00     NaN
1969-04-30 16:20:00    10.0
1969-05-01 11:00:00     5.0
1975-04-19 16:30:00     0.0
1978-04-25 12:00:00     2.0
                       ... 
2021-07-14 22:45:00     NaN
2021-07-14 23:00:00     NaN
2021-07-14 23:15:00     NaN
2021-07-14 23:30:00     NaN
2021-07-14 23:45:00     NaN
Name: Color, water, fltrd, Pt-Co units(00080), Length: 76244, dtype: float64

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# Plot: Color Colville Yearly
 
fig, ax = plt.subplots(figsize=(12, 8))
ax.plot(Colville.Datetime.values,
        Colville['Color, water, fltrd, Pt-Co units(00080)'].values,
        '-o',
        color='blue')
ax.set(xlabel="Date", ylabel="Color of water (filtered) Pt-Co units ",
       title="Color of water (filtered) in the Colville River Monthly")
#ax.set_xticks((Colville['Color, water, fltrd, Pt-Co units(00080)'].values))
labels = ['1969-04-30 16:20:00', '1969-05-01 11:00:00', '1975-04-19 16:30:00','1978-04-25 12:00:00']
 
#ax.set_xticks(Colville.Datetime.values, labels)
 
#plt.xticks(ticks=Colville.Datetime.values, labels=labels)
 
 
plt.show()

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# Plot: Color Colville Yearly
 
# Create dummy variable for non NaN Colville
Colville_dummy = Colville[['month','year','Color, water, fltrd, Pt-Co units(00080)','Datetime']].copy()
 
# Get rid of all data that is NaN
Colville_dummy = Colville_dummy.dropna(subset = ['Color, water, fltrd, Pt-Co units(00080)'])
print(Colville_dummy)
 
# Set index to year
#Colville_dummy.index.name
 
# Plot Colville Color Data
sns.scatterplot(Colville_dummy['year'], Colville_dummy['Color, water, fltrd, Pt-Co units(00080)'],data = Colville_dummy, palette='Set1',style='Color, water, fltrd, Pt-Co units(00080)', hue = 'Color, water, fltrd, Pt-Co units(00080)',s =100, legend = 'full') #label = Colville_dummy.index,hue
plt.legend(Colville_dummy['Datetime'])
plt.show()

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# Plot: Color Colville monthly 
# Create dummy variable for non NaN Colville
 
# Get rid of all data that is NaN
Colville_dummy = Colville_dummy.dropna(subset = ['Color, water, fltrd, Pt-Co units(00080)'])
Colville_dummy = Colville[['month','year','Color, water, fltrd, Pt-Co units(00080)','Datetime']].copy()
print(Colville_dummy)
 
# Plot Colville Color Data
sns.scatterplot(Colville_dummy['month'], Colville_dummy['Color, water, fltrd, Pt-Co units(00080)'], data = Colville_dummy, style='Color, water, fltrd, Pt-Co units(00080)', hue = 'Color, water, fltrd, Pt-Co units(00080)', palette='Set1', s =100) #label = Colville_dummy.index,hue
 
# Set index to year
#Colville_dummy.index.name
plt.legend(Colville_dummy['Datetime'])
 
plt.xticks(range(1,13))
plt.show()

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# Plot: Nitrate Colville Yearly
 
fig, ax = plt.subplots(figsize=(12, 8))
ax.plot(Colville.Datetime.values,
        Colville['Nitrate water, fltrd,  mg/L  as N(00618)'].values,
        '-o',
        color='blue')
ax.set(xlabel="Date", ylabel="Nitrate water, fltrd,  mg/L  as N(00618)",
       title="Nitrate water, fltrd,  mg/L  as N(00618) in the Colville River Monthly")
 
#ax.set_xticks((Colville['Color, water, fltrd, Pt-Co units(00080)'].values))
#ax.set_xticklabels(('0','1969-04-30 16:20:00', '1969-05-01 11:00:00', '1975-04-19 16:30:00','1978-04-25 12:00:00'), rotation = 45)
 
plt.show()
 
 

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# Plot: Nitrate Colville Yearly
 
# Create dummy variable for non NaN Colville
Colville_dummy = Colville[['month','year','Nitrate water, fltrd,  mg/L  as N(00618)']].copy()
 
# Get rid of all data that is NaN
Colville_dummy = Colville_dummy.dropna(subset = ['Nitrate water, fltrd,  mg/L  as N(00618)'])
print(Colville_dummy)
 
# Plot Colville Color Data
sns.scatterplot(Colville_dummy['year'], Colville_dummy['Nitrate water, fltrd,  mg/L  as N(00618)'], data = Colville_dummy, hue = 'Nitrate water, fltrd,  mg/L  as N(00618)',style='Nitrate water, fltrd,  mg/L  as N(00618)', s =100, palette='Set1', legend = 'full') #label = Colville_dummy.index,hue
plt.legend(Colville_dummy.index)
plt.show()

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# Plot: Nitrate Colville Monthly
Colville_dummy = Colville[['month','year','Nitrate water, fltrd,  mg/L  as N(00618)']].copy()
 
# Get rid of all data that is NaN
Colville_dummy = Colville_dummy.dropna(subset = ['Nitrate water, fltrd,  mg/L  as N(00618)'])
print(Colville_dummy)
 
# Set index to year
#Colville_dummy.index.name
 
# Plot Colville Color Data
sns.scatterplot(Colville_dummy['month'], Colville_dummy['Nitrate water, fltrd,  mg/L  as N(00618)'], data = Colville_dummy, style='Nitrate water, fltrd,  mg/L  as N(00618)', hue = 'Nitrate water, fltrd,  mg/L  as N(00618)', palette='Set1', s =100, legend = 'full') #label = Colville_dummy.index,hue
plt.legend(Colville_dummy.index)
plt.show()
 
 
# Create dummy variable for non NaN Colville
plt.xticks(range(1,13))
 

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# Plot: Organic_carbon Colville Yearly
 
fig, ax = plt.subplots(figsize=(12, 8))
ax.plot(Colville.Datetime.values,
        Colville['Organic_carbon, water,unfltrd  mg/L(00680)'].values,
        '-o',
        color='blue')
ax.set(xlabel="Date", ylabel="Organic_carbon, water,unfltrd  mg/L(00680)",
       title="Organic_carbon, water,unfltrd  mg/L(00680) in the Colville River Monthly")
 
#ax.set_xticks((Colville['Color, water, fltrd, Pt-Co units(00080)'].values))
#ax.set_xticklabels(('0','1969-04-30 16:20:00', '1969-05-01 11:00:00', '1975-04-19 16:30:00','1978-04-25 12:00:00'), rotation = 45)
 
plt.show()

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# Plot: Organic_carbon Colville Yearly
 
# Create dummy variable for non NaN Colville
Colville_dummy = Colville[['month','year','Organic_carbon, water,unfltrd  mg/L(00680)']].copy()
 
# Get rid of all data that is NaN
Colville_dummy = Colville_dummy.dropna(subset = ['Organic_carbon, water,unfltrd  mg/L(00680)'])
print(Colville_dummy)
 
# Set index to year
#Colville_dummy.index.name
 
# Plot Colville Color Data
sns.scatterplot(Colville_dummy['year'], Colville_dummy['Organic_carbon, water,unfltrd  mg/L(00680)'], data = Colville_dummy, style='Organic_carbon, water,unfltrd  mg/L(00680)', hue = 'Organic_carbon, water,unfltrd  mg/L(00680)', palette='Set1', s =100, legend = 'full') #label = Colville_dummy.index,hue
plt.legend(Colville_dummy.index)
plt.show()

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# Plot: Organic_carbon Colville Monthly
 
# Create dummy variable for non NaN Colville
Colville_dummy = Colville[['month','year','Organic_carbon, water,unfltrd  mg/L(00680)']].copy()
 
# Get rid of all data that is NaN
Colville_dummy = Colville_dummy.dropna(subset = ['Organic_carbon, water,unfltrd  mg/L(00680)'])
print(Colville_dummy)
 
# Set index to year
#Colville_dummy.index.name
 
# Plot Colville Color Data
sns.scatterplot(Colville_dummy['month'], Colville_dummy['Organic_carbon, water,unfltrd  mg/L(00680)'], data = Colville_dummy, style='Organic_carbon, water,unfltrd  mg/L(00680)', hue = 'Organic_carbon, water,unfltrd  mg/L(00680)', palette='Set1', s =100, legend = 'full') #label = Colville_dummy.index,hue
plt.legend(Colville_dummy.index)
plt.xticks(range(1,13))
plt.show()

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Kuparuk

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Discharge

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# Plot: Discharge Kuparuk
plt.scatter(Kuparuk.loc['2016']['month'],Kuparuk.loc['2016']['Discharge_ft2/s'],color = 'red', label = '2016')
plt.scatter(Kuparuk.loc['2017']['month'],Kuparuk.loc['2017']['Discharge_ft2/s'],color = 'orange', label = '2017')
plt.scatter(Kuparuk.loc['2018']['month'],Kuparuk.loc['2018']['Discharge_ft2/s'],color = 'yellow', label = '2018')
plt.scatter(Kuparuk.loc['2019']['month'],Kuparuk.loc['2019']['Discharge_ft2/s'],color = 'green', label = '2019')
plt.scatter(Kuparuk.loc['2020']['month'],Kuparuk.loc['2020']['Discharge_ft2/s'],color = 'blue', label = '2020')
plt.scatter(Kuparuk.loc['2021']['month'],Kuparuk.loc['2021']['Discharge_ft2/s'],color = 'purple', label = '2021')
 
plt.legend()
plt.xlabel('Months')
plt.ylabel('Discharge ft2/s')
plt.title('Discharge of the Kuparuk River monthly 2016-2021')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.xticks(range(1,13))
 
plt.show()
 

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Precipitation

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Air Temperature

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Gageheight

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# Plot: Gageheight Kuparuk
plt.scatter(Kuparuk.loc['2016']['month'],Kuparuk.loc['2016']['Gageheight_ft'],color = 'red', label = '2016')
plt.scatter(Kuparuk.loc['2017']['month'],Kuparuk.loc['2017']['Gageheight_ft'],color = 'orange', label = '2017')
plt.scatter(Kuparuk.loc['2018']['month'],Kuparuk.loc['2018']['Gageheight_ft'],color = 'yellow', label = '2018')
plt.scatter(Kuparuk.loc['2019']['month'],Kuparuk.loc['2019']['Gageheight_ft'],color = 'green', label = '2019')
plt.scatter(Kuparuk.loc['2020']['month'],Kuparuk.loc['2020']['Gageheight_ft'],color = 'blue', label = '2020')
plt.scatter(Kuparuk.loc['2021']['month'],Kuparuk.loc['2021']['Gageheight_ft'],color = 'purple', label = '2021')
 
plt.legend()
plt.xlabel('Months')
plt.ylabel('Gageheight (ft)')
plt.title('Gageheight (ft) of the Kuparuk River monthly 2016-2021')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.xticks(range(1,13))
plt.show()
 

Enter to Rename, ⇧Enter to Preview

Color, Nitrate, Organic Carbon

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Index(['Sampling depth (ft)(00003)', 'Stream width (ft)',
       'Temperature,water(C)(00010)', 'Temperature,air(C)(00020)',
       'Barometric pressure, (mmHg)(00025)', 'Surface area,(mi2)(00049)',
       'Instantaneous_discharge(ft3/s)(00061)', 'Gageheight_feet(00065)',
       'Turbidity,JTU(00070)', 'Turbidity,NTU(00076)',
       ...
       'Discharge_ft2/s', 'Precipitation_total_in', 'Temperature_air_C',
       'Gageheight_ft', 'Gageheight_ft_2', 'Datetime', 'year', 'month', 'day',
       'time'],
      dtype='object', length=159)

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# Plot: Color Kuparuk Yearly
import datetime
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Color, water, fltrd, Pt-Co units(00080)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Color, water, fltrd, Pt-Co units(00080)'])
print(Kuparuk_dummy)
 
# Plot Kuparuk Color Data
sns.scatterplot(Kuparuk_dummy['year'], Kuparuk_dummy['Color, water, fltrd, Pt-Co units(00080)'],data = Kuparuk_dummy,hue = 'month' ,palette='Set1', s =100) 
plt.legend(Kuparuk_dummy['Datetime'])
plt.show()

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# Plot: Color Kuparuk months
 
fig, ax = plt.subplots(figsize=(12, 8))
ax.plot(Kuparuk.Datetime.values,
        Kuparuk['Color, water, fltrd, Pt-Co units(00080)'].values,
        '-o',
        color='blue')
ax.set(xlabel="Date", ylabel="Color of water (filtered) Pt-Co units ",
       title="Color of water (filtered) in the Kuparuk River Yearly")
 
plt.show()
 

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# Plot: Color Kuparuk Monthly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Color, water, fltrd, Pt-Co units(00080)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Color, water, fltrd, Pt-Co units(00080)'])
print(Kuparuk_dummy)
 
# Plot Kuparuk Color Data
sns.scatterplot(Kuparuk_dummy['month'], Kuparuk_dummy['Color, water, fltrd, Pt-Co units(00080)'],data = Kuparuk_dummy,hue = 'year' ,palette='Set1', s =100) 
plt.legend(Kuparuk_dummy['Datetime'])
plt.xticks(range(1,13))
plt.show()

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['Sampling depth (ft)(00003)',
 'Stream width (ft)',
 'Temperature,water(C)(00010)',
 'Temperature,air(C)(00020)',
 'Barometric pressure, (mmHg)(00025)',
 'Surface area,(mi2)(00049)',
 'Instantaneous_discharge(ft3/s)(00061)',
 'Gageheight_feet(00065)',
 'Turbidity,JTU(00070)',
 'Turbidity,NTU(00076)',
 'Color, water, fltrd, Pt-Co units(00080)',
 'Specific conductance,wat unfuS/cm @25 degC(00095)',
 'Hydrogen_ion, water,unfltrd calcd,  mg/L(00191)',
 'Dissolved_oxygen,(mg/L)(00300)',
 'Dissolved_oxygen,percentof sat-uration(00301)',
 'pH, water,unfltrd field, std units(00400)',
 'pH, water,unfltrd lab, std units(00403)',
 'Carbon_dioxide water,unfltrd  mg/L(00405)',
 'Bicarbonate,wat unf fixedend pt, field,  mg/L(00440)',
 'Carbonate,wat unf fixedend pt, field,  mg/L(00445)',
 'Peri-phytonbiomass ashweight, g/m2(00572)',
 'Peri-phytonbiomass dryweight, g/m2(00573)',
 'Total nitrogen, water,unfltrd mg/L(00600)',
 'Total nitrogen, water, fltrd, mg/L(00602)',
 'Organic nitrogen, water,unfltrd  mg/L(00605)',
 'Organic nitrogen, water, fltrd,  mg/L(00607)',
 'Ammonia water, fltrd,  mg/L  as N(00608)',
 'Ammonia water,unfltrd  mg/L  as N(00610)',
 'Nitrite water, fltrd, mg/L  as N(00613)',
 'Nitrate water, fltrd,  mg/L  as N(00618)',
 'Ammonia + org-N, water, fltrd, mg/L  as N(00623)',
 'Ammonia + org-N,sus sed total, mg/L  as N(00624)',
 'Ammonia + org-N, water,unfltrd mg/L  as N(00625)',
 'Nitrate + nitrite water,unfltrd mg/L  as N(00630)',
 'Nitrate + nitrite water, fltrd, mg/L  as N(00631)',
 'Phosphate, water,unfltrd mg/L(00650)',
 'Orthophosphate, water, fltrd, mg/L(00660)',
 'Phosphorus, water,unfltrd mg/L  as P(00665)',
 'Phosphorus, water, fltrd, mg/L  as P(00666)',
 'Orthophosphate, water, fltrd, mg/L  as P(00671)',
 'Organiccarbon, water,unfltrd mg/L(00680)',
 'Organiccarbon, water, fltrd, mg/L(00681)',
 'Organiccarbon, suspndsedimnt total, mg/L(00689)',
 'Hardness, water,mg/L as CaCO3(00900)',
 'Noncarb hardness,wat unf field,mg/L as CaCO3(00902)',
 'Calcium water, fltrd, mg/L(00915)',
 'Magnesium, water, fltrd, mg/L(00925)',
 'Sodium, water, fltrd, mg/L(00930)',
 'Sodium adsorption ratio(00931)',
 'Sodium fraction of cations percent(00932)',
 'Sodium + potassium, water, fltrd, mg/L as Na(00933)',
 'Potassium, water, fltrd, mg/L(00935)',
 'Chloride, water, fltrd, mg/L(00940)',
 'Sulfate water, fltrd, mg/L(00945)',
 'Fluoride, water, fltrd, mg/L(00950)',
 'Silica, water, fltrd,mg/L as  SiO2(00955)',
 'Arsenic water, fltrd, ug/L(01000)',
 'Arsenic suspndsedimnt total,  ug/L(01001)',
 'Arsenic water,unfltrd ug/L(01002)',
 'Barium, water, fltrd, ug/L(01005)',
 'Barium, suspndsedimntrecover -able,  ug/L(01006)',
 'Barium, water,unfltrdrecover -able,  ug/L(01007)',
 'Beryllium, water, fltrd, ug/L(01010)',
 'Cadmium water, fltrd, ug/L(01025)',
 'Cadmium suspndsedimntrecover -able,  ug/L(01026)',
 'Cadmium water,unfltrd ug/L(01027)',
 'Chromium, water, fltrd,  ug/L(01030)',
 'Chromium, suspndsedimntrecover -able,  ug/L(01031)',
 'Chromium, water,unfltrdrecover -able,  ug/L(01034)',
 'Cobalt water, fltrd,  ug/L(01035)',
 'Cobalt suspndsedimntrecover -able,  ug/L(01036)',
 'Cobalt water,unfltrdrecover -able,  ug/L(01037)',
 'Copper, water, fltrd,  ug/L(01040)',
 'Copper, suspndsedimntrecover -able,  ug/L(01041)',
 'Copper, water,unfltrdrecover -able,  ug/L(01042)',
 'Iron, suspndsedimntrecover -able,  ug/L(01044)',
 'Iron, water,unfltrdrecover -able,  ug/L(01045)',
 'Iron, water, fltrd,  ug/L(01046)',
 'Lead, water, fltrd,  ug/L(01049)',
 'Lead, suspndsedimntrecover -able,  ug/L(01050)',
 'Lead, water,unfltrdrecover -able,  ug/L(01051)',
 'Manganese, suspndsedimntrecover -able,  ug/L(01054)',
 'Manganese, water,unfltrdrecover -able,  ug/L(01055)',
 'Manganese, water, fltrd,  ug/L(01056)',
 'Molybdenum, water, fltrd,  ug/L(01060)',
 'Nickel, water, fltrd,  ug/L(01065)',
 'Nickel, suspndsedimntrecover -able,  ug/L(01066)',
 'Nickel, water,unfltrdrecover -able,  ug/L(01067)',
 'Silver, water, fltrd,  ug/L(01075)',
 'Silver, suspndsedimntrecover -able,  ug/L(01076)',
 'Silver, water,unfltrdrecover -able,  ug/L(01077)',
 'Strontium, water, fltrd,  ug/L(01080)',
 'Vanadium, water, fltrd,  ug/L(01085)',
 'Zinc, water, fltrd,  ug/L(01090)',
 'Zinc, suspndsedimntrecover -able,  ug/L(01091)',
 'Zinc, water,unfltrdrecover -able,  ug/L(01092)',
 'Aluminum, water, fltrd,  ug/L(01106)',
 'Lithium water, fltrd,  ug/L(01130)',
 'Selenium, water, fltrd,  ug/L(01145)',
 'Selenium, suspndsedimnt total,  ug/L(01146)',
 'Selenium, water,unfltrd  ug/L(01147)',
 'Gage height above datum meters(30207)',
 'Discharge,instantaneous  m3/s(30209)',
 'Fecal coli- form,  M-FC0.45uMF  cfu/ 100 mL(31616)',
 'Fecal coli- form,  M-FC0.7u MF  cfu/ 100 mL(31625)',
 'Fecal strep-tococci   KF  MF,  cfu/ 100 mL(31673)',
 'Fecal strep-tococcim-Enterococcus  col/ 100 mL(31679)',
 'Chlorophyll b periphyton,uncorr, mg/m2(32226)',
 'Chlorophyll a periphyton,uncorr, mg/m2(32228)',
 'Biomass peri-phyton,ashfreedrymass  g/m2(49954)',
 'Phytoplankton, total, cells  /mL(60050)',
 'Dissolved solids,dried @180degCwat flt  mg/L(70300)',
 'Dissolved solids,sum  ofconsti-tuents,  mg/L(70301)',
 'Dissolved solids,water, ton/d(70302)',
 'Dissolved solids,water, tons/acre-ft(70303)',
 'Suspnd. sediment sievediametrpercent<0.0625   mm(70331)',
 'Suspnd. sediment, sievediametrpercent<.125mm(70332)',
 'Suspnd. sediment, sievediametrpercent <.25mm(70333)',
 'Suspnd. sediment, sievediametrpercent <.5 mm(70334)',
 'Suspnd. sediment,falldiadst watpercent<.002mm(70337)',
 'Suspnd. sediment,falldiadst watpercent<.004mm(70338)',
 'Suspnd. sediment,falldiadst watpercent<.008mm(70339)',
 'Suspnd. sediment,falldiadst watpercent<.016mm(70340)',
 'Suspnd. sediment,falldiadst watpercent<.031mm(70341)',
 'Orthophosphate, water,unfltrd  mg/L  as P(70507)',
 'Biomasschlorophyll ratio, peri-phyton, number(70950)',
 'Chloro-phyll a periphyton,chromo-fluoro, mg/m2(70957)',
 'Chloro-phyll b periphyton,chromo-fluoro, mg/m2(70958)',
 'Ammonia water, unfltrd  mg/L(71845)',
 'Ammonia water, fltrd,  mg/L(71846)',
 'Nitrate water, fltrd,  mg/L(71851)',
 'Nitrite water, fltrd,  mg/L as NO2(71856)',
 'Manga- nese, water,unfltrd  ug/L(71883)',
 'Iron, water,unfltrd  ug/L(71885)',
 'Phosphorus, water,unfltrd  mg/L as PO4(71886)',
 'Total nitrogen, water,unfltrd  mg/L as NO3(71887)',
 'Mercury water, fltrd,  ug/L(71890)',
 'Mercury suspndsedimntrecover -able,  ug/L(71895)',
 'Mercury water,unfltrdrecover -able,  ug/L(71900)',
 'Suspended sediment concentration  mg/L(80154)',
 'Suspended sediment  discharge, ton/d(80155)',
 'Carbonate,wat unfinf tit field,mg/L as CaCO3(99430)',
 'Bicarbonate,wat unfinfl pt titr., field, mg/L(99440)',
 'Year',
 'Temperature_water_(C)',
 'Discharge_(ft3/s)',
 'Stream_flow(ft3/s)',
 'Gage_height(ft)',
 'GH_Change(ft)',
 'Discharge_ft2/s',
 'Precipitation_total_in',
 'Temperature_air_C',
 'Gageheight_ft',
 'Gageheight_ft_2',
 'Datetime',
 'year',
 'month',
 'day',
 'time']

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# Plot: Nitrate Kuparuk Yearly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Nitrate water, fltrd,  mg/L(71851)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Nitrate water, fltrd,  mg/L(71851)'])
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
 
Kuparuk_dummy = Kuparuk_dummy.replace("<0.443", "0.443")
Kuparuk_dummy['Nitrate water, fltrd,  mg/L(71851)'] = Kuparuk_dummy['Nitrate water, fltrd,  mg/L(71851)'].astype(float)

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fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['year'],y = Kuparuk_dummy['Nitrate water, fltrd,  mg/L(71851)'],data = Kuparuk_dummy,hue = 'month',palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1))
 
ax.set_ylim([-0.5,2.5])
ax.set_yticks((0,0.5,1.0,1.5,2.0))
#ax.set_yticks((2, 5, 7, 10))
ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
#plt.gca().invert_yaxis()
 
plt.show()

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# Plot Kuparuk Nitrate Data Monthly
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['month'],y = Kuparuk_dummy['Nitrate water, fltrd,  mg/L(71851)'],data = Kuparuk_dummy,hue = 'year' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
#ax.legend(Kuparuk_dummy['month'])/'
ax.set_yticks((0,0.5,1.0,1.5,2.0))
ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
#plt.gca().invert_yaxis()
 
 
#ax.set_yticks((2, 5, 7, 10))
ax.set_ylim([-0.5,2.5])
           borderaxespad=0)
plt.show()
 
 

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# Plot: Organic_carbon Kuparuk Yearly
 
fig, ax = plt.subplots(figsize=(12, 8))
ax.plot(Kuparuk.Datetime.values,
        Kuparuk['Organiccarbon, water,unfltrd mg/L(00680)'].values,
        '-o',
        color='blue')
ax.set(xlabel="Date", ylabel="Organic_carbon, water,unfltrd  mg/L(00680)",
       title="Organic_carbon, water,unfltrd  mg/L(00680) in the Kuparuk River Monthly")
 
#ax.set_xticks((Colville['Color, water, fltrd, Pt-Co units(00080)'].values))
#ax.set_xticklabels(('0','1969-04-30 16:20:00', '1969-05-01 11:00:00', '1975-04-19 16:30:00','1978-04-25 12:00:00'), rotation = 45)
 
plt.show()

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# Plot: Organic_carbon Kuparuk Yearly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Organiccarbon, water,unfltrd mg/L(00680)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Organiccarbon, water,unfltrd mg/L(00680)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['year'],y = Kuparuk_dummy['Organiccarbon, water,unfltrd mg/L(00680)'],data = Kuparuk_dummy,hue = 'month' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
#plt.gca().invert_yaxis()
plt.show()
           borderaxespad=0)
 

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# Plot: Organic_carbon Kuparuk monthly
 
plt.scatter(Kuparuk['month'],Kuparuk['Organiccarbon, water,unfltrd mg/L(00680)'],color = 'blue', label = 'Colville')
plt.legend()
plt.xlabel('Month')
plt.ylabel('Organic carbon (filtered) mg/L ')
plt.title('Organic carbon (filtered) mg/L in the Kuparuk River Monthly')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.xticks(range(1,13))
plt.show()

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# Plot: Organic_carbon Kuparuk Monthly
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['month'],y = Kuparuk_dummy['Organiccarbon, water,unfltrd mg/L(00680)'],data = Kuparuk_dummy,hue = 'year' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
#plt.gca().invert_yaxis()
plt.show()
           borderaxespad=0)
plt.xticks(range(0,13))
 

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Chlorophyll & Phytoplankton

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['Sampling depth (ft)(00003)',
 'Stream width (ft)',
 'Temperature,water(C)(00010)',
 'Temperature,air(C)(00020)',
 'Barometric pressure, (mmHg)(00025)',
 'Surface area,(mi2)(00049)',
 'Instantaneous_discharge(ft3/s)(00061)',
 'Gageheight_feet(00065)',
 'Turbidity,JTU(00070)',
 'Turbidity,NTU(00076)',
 'Color, water, fltrd, Pt-Co units(00080)',
 'Specific conductance,wat unfuS/cm @25 degC(00095)',
 'Hydrogen_ion, water,unfltrd calcd,  mg/L(00191)',
 'Dissolved_oxygen,(mg/L)(00300)',
 'Dissolved_oxygen,percentof sat-uration(00301)',
 'pH, water,unfltrd field, std units(00400)',
 'pH, water,unfltrd lab, std units(00403)',
 'Carbon_dioxide water,unfltrd  mg/L(00405)',
 'Bicarbonate,wat unf fixedend pt, field,  mg/L(00440)',
 'Carbonate,wat unf fixedend pt, field,  mg/L(00445)',
 'Peri-phytonbiomass ashweight, g/m2(00572)',
 'Peri-phytonbiomass dryweight, g/m2(00573)',
 'Total nitrogen, water,unfltrd mg/L(00600)',
 'Total nitrogen, water, fltrd, mg/L(00602)',
 'Organic nitrogen, water,unfltrd  mg/L(00605)',
 'Organic nitrogen, water, fltrd,  mg/L(00607)',
 'Ammonia water, fltrd,  mg/L  as N(00608)',
 'Ammonia water,unfltrd  mg/L  as N(00610)',
 'Nitrite water, fltrd, mg/L  as N(00613)',
 'Nitrate water, fltrd,  mg/L  as N(00618)',
 'Ammonia + org-N, water, fltrd, mg/L  as N(00623)',
 'Ammonia + org-N,sus sed total, mg/L  as N(00624)',
 'Ammonia + org-N, water,unfltrd mg/L  as N(00625)',
 'Nitrate + nitrite water,unfltrd mg/L  as N(00630)',
 'Nitrate + nitrite water, fltrd, mg/L  as N(00631)',
 'Phosphate, water,unfltrd mg/L(00650)',
 'Orthophosphate, water, fltrd, mg/L(00660)',
 'Phosphorus, water,unfltrd mg/L  as P(00665)',
 'Phosphorus, water, fltrd, mg/L  as P(00666)',
 'Orthophosphate, water, fltrd, mg/L  as P(00671)',
 'Organiccarbon, water,unfltrd mg/L(00680)',
 'Organiccarbon, water, fltrd, mg/L(00681)',
 'Organiccarbon, suspndsedimnt total, mg/L(00689)',
 'Hardness, water,mg/L as CaCO3(00900)',
 'Noncarb hardness,wat unf field,mg/L as CaCO3(00902)',
 'Calcium water, fltrd, mg/L(00915)',
 'Magnesium, water, fltrd, mg/L(00925)',
 'Sodium, water, fltrd, mg/L(00930)',
 'Sodium adsorption ratio(00931)',
 'Sodium fraction of cations percent(00932)',
 'Sodium + potassium, water, fltrd, mg/L as Na(00933)',
 'Potassium, water, fltrd, mg/L(00935)',
 'Chloride, water, fltrd, mg/L(00940)',
 'Sulfate water, fltrd, mg/L(00945)',
 'Fluoride, water, fltrd, mg/L(00950)',
 'Silica, water, fltrd,mg/L as  SiO2(00955)',
 'Arsenic water, fltrd, ug/L(01000)',
 'Arsenic suspndsedimnt total,  ug/L(01001)',
 'Arsenic water,unfltrd ug/L(01002)',
 'Barium, water, fltrd, ug/L(01005)',
 'Barium, suspndsedimntrecover -able,  ug/L(01006)',
 'Barium, water,unfltrdrecover -able,  ug/L(01007)',
 'Beryllium, water, fltrd, ug/L(01010)',
 'Cadmium water, fltrd, ug/L(01025)',
 'Cadmium suspndsedimntrecover -able,  ug/L(01026)',
 'Cadmium water,unfltrd ug/L(01027)',
 'Chromium, water, fltrd,  ug/L(01030)',
 'Chromium, suspndsedimntrecover -able,  ug/L(01031)',
 'Chromium, water,unfltrdrecover -able,  ug/L(01034)',
 'Cobalt water, fltrd,  ug/L(01035)',
 'Cobalt suspndsedimntrecover -able,  ug/L(01036)',
 'Cobalt water,unfltrdrecover -able,  ug/L(01037)',
 'Copper, water, fltrd,  ug/L(01040)',
 'Copper, suspndsedimntrecover -able,  ug/L(01041)',
 'Copper, water,unfltrdrecover -able,  ug/L(01042)',
 'Iron, suspndsedimntrecover -able,  ug/L(01044)',
 'Iron, water,unfltrdrecover -able,  ug/L(01045)',
 'Iron, water, fltrd,  ug/L(01046)',
 'Lead, water, fltrd,  ug/L(01049)',
 'Lead, suspndsedimntrecover -able,  ug/L(01050)',
 'Lead, water,unfltrdrecover -able,  ug/L(01051)',
 'Manganese, suspndsedimntrecover -able,  ug/L(01054)',
 'Manganese, water,unfltrdrecover -able,  ug/L(01055)',
 'Manganese, water, fltrd,  ug/L(01056)',
 'Molybdenum, water, fltrd,  ug/L(01060)',
 'Nickel, water, fltrd,  ug/L(01065)',
 'Nickel, suspndsedimntrecover -able,  ug/L(01066)',
 'Nickel, water,unfltrdrecover -able,  ug/L(01067)',
 'Silver, water, fltrd,  ug/L(01075)',
 'Silver, suspndsedimntrecover -able,  ug/L(01076)',
 'Silver, water,unfltrdrecover -able,  ug/L(01077)',
 'Strontium, water, fltrd,  ug/L(01080)',
 'Vanadium, water, fltrd,  ug/L(01085)',
 'Zinc, water, fltrd,  ug/L(01090)',
 'Zinc, suspndsedimntrecover -able,  ug/L(01091)',
 'Zinc, water,unfltrdrecover -able,  ug/L(01092)',
 'Aluminum, water, fltrd,  ug/L(01106)',
 'Lithium water, fltrd,  ug/L(01130)',
 'Selenium, water, fltrd,  ug/L(01145)',
 'Selenium, suspndsedimnt total,  ug/L(01146)',
 'Selenium, water,unfltrd  ug/L(01147)',
 'Gage height above datum meters(30207)',
 'Discharge,instantaneous  m3/s(30209)',
 'Fecal coli- form,  M-FC0.45uMF  cfu/ 100 mL(31616)',
 'Fecal coli- form,  M-FC0.7u MF  cfu/ 100 mL(31625)',
 'Fecal strep-tococci   KF  MF,  cfu/ 100 mL(31673)',
 'Fecal strep-tococcim-Enterococcus  col/ 100 mL(31679)',
 'Chlorophyll b periphyton,uncorr, mg/m2(32226)',
 'Chlorophyll a periphyton,uncorr, mg/m2(32228)',
 'Biomass peri-phyton,ashfreedrymass  g/m2(49954)',
 'Phytoplankton, total, cells  /mL(60050)',
 'Dissolved solids,dried @180degCwat flt  mg/L(70300)',
 'Dissolved solids,sum  ofconsti-tuents,  mg/L(70301)',
 'Dissolved solids,water, ton/d(70302)',
 'Dissolved solids,water, tons/acre-ft(70303)',
 'Suspnd. sediment sievediametrpercent<0.0625   mm(70331)',
 'Suspnd. sediment, sievediametrpercent<.125mm(70332)',
 'Suspnd. sediment, sievediametrpercent <.25mm(70333)',
 'Suspnd. sediment, sievediametrpercent <.5 mm(70334)',
 'Suspnd. sediment,falldiadst watpercent<.002mm(70337)',
 'Suspnd. sediment,falldiadst watpercent<.004mm(70338)',
 'Suspnd. sediment,falldiadst watpercent<.008mm(70339)',
 'Suspnd. sediment,falldiadst watpercent<.016mm(70340)',
 'Suspnd. sediment,falldiadst watpercent<.031mm(70341)',
 'Orthophosphate, water,unfltrd  mg/L  as P(70507)',
 'Biomasschlorophyll ratio, peri-phyton, number(70950)',
 'Chloro-phyll a periphyton,chromo-fluoro, mg/m2(70957)',
 'Chloro-phyll b periphyton,chromo-fluoro, mg/m2(70958)',
 'Ammonia water, unfltrd  mg/L(71845)',
 'Ammonia water, fltrd,  mg/L(71846)',
 'Nitrate water, fltrd,  mg/L(71851)',
 'Nitrite water, fltrd,  mg/L as NO2(71856)',
 'Manga- nese, water,unfltrd  ug/L(71883)',
 'Iron, water,unfltrd  ug/L(71885)',
 'Phosphorus, water,unfltrd  mg/L as PO4(71886)',
 'Total nitrogen, water,unfltrd  mg/L as NO3(71887)',
 'Mercury water, fltrd,  ug/L(71890)',
 'Mercury suspndsedimntrecover -able,  ug/L(71895)',
 'Mercury water,unfltrdrecover -able,  ug/L(71900)',
 'Suspended sediment concentration  mg/L(80154)',
 'Suspended sediment  discharge, ton/d(80155)',
 'Carbonate,wat unfinf tit field,mg/L as CaCO3(99430)',
 'Bicarbonate,wat unfinfl pt titr., field, mg/L(99440)',
 'Year',
 'Temperature_water_(C)',
 'Discharge_(ft3/s)',
 'Stream_flow(ft3/s)',
 'Gage_height(ft)',
 'GH_Change(ft)',
 'Discharge_ft2/s',
 'Precipitation_total_in',
 'Temperature_air_C',
 'Gageheight_ft',
 'Gageheight_ft_2',
 'Datetime',
 'year',
 'month',
 'day',
 'time']

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# Plot: Chlorophyll a (Chromo-fluoro) Kuparuk Yearly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Chloro-phyll a periphyton,chromo-fluoro, mg/m2(70957)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Chloro-phyll a periphyton,chromo-fluoro, mg/m2(70957)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['year'],y = Kuparuk_dummy['Chloro-phyll a periphyton,chromo-fluoro, mg/m2(70957)'],data = Kuparuk_dummy,hue = 'month' ,palette='Set1', s =100) 
           borderaxespad=0)
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.f'))
plt.gca().invert_yaxis()
plt.show()
 
#ax.legend(Kuparuk_dummy['month'])
ax.set_xticks((1977,1978,1979))
 
 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
 

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# Plot: Chlorophyll a (Chromo-fluoro) Kuparuk Monthly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Chloro-phyll a periphyton,chromo-fluoro, mg/m2(70957)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Chloro-phyll a periphyton,chromo-fluoro, mg/m2(70957)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['month'],y = Kuparuk_dummy['Chloro-phyll a periphyton,chromo-fluoro, mg/m2(70957)'],data = Kuparuk_dummy,hue = 'year' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
           borderaxespad=0)
 
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.f'))
plt.gca().invert_yaxis()
ax.set_xticks(range(0,13))
 
 
plt.show()

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# Plot: Chlorophyll b (Chromo-fluoro) Kuparuk Yearly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Chloro-phyll b periphyton,chromo-fluoro, mg/m2(70958)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Chloro-phyll b periphyton,chromo-fluoro, mg/m2(70958)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['year'],y = Kuparuk_dummy['Chloro-phyll b periphyton,chromo-fluoro, mg/m2(70958)'],data = Kuparuk_dummy,hue = 'year' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
           borderaxespad=0)
 
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.f'))
plt.gca().invert_yaxis()
ax.set_xticks((1977,1978,1979))
 
 
plt.show()
 

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# Plot: Chlorophyll b (Chromo-fluoro) Kuparuk Monthly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Chloro-phyll b periphyton,chromo-fluoro, mg/m2(70958)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Chloro-phyll b periphyton,chromo-fluoro, mg/m2(70958)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['month'],y = Kuparuk_dummy['Chloro-phyll b periphyton,chromo-fluoro, mg/m2(70958)'],data = Kuparuk_dummy,hue = 'year' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
           borderaxespad=0)
 
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.f'))
plt.gca().invert_yaxis()
ax.set_xticks(range(0,13))
 
 
plt.show()

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# Plot: Chlorophyll a periphyton,uncorr  mg/m2 Kuparuk Yearly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Chlorophyll a periphyton,uncorr, mg/m2(32228)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Chlorophyll a periphyton,uncorr, mg/m2(32228)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['year'],y = Kuparuk_dummy['Chlorophyll a periphyton,uncorr, mg/m2(32228)'],data = Kuparuk_dummy,hue = 'month' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
           borderaxespad=0)
 
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.f'))
plt.gca().invert_yaxis()
ax.set_xticks((1974,1975,1976,1977,1978))
 
 
plt.show()
 

# Plot: Chlorophyll a periphyton,uncorr  mg/m2 Kuparuk Monthly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Chlorophyll a periphyton,uncorr, mg/m2(32228)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Chlorophyll a periphyton,uncorr, mg/m2(32228)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['month'],y = Kuparuk_dummy['Chlorophyll a periphyton,uncorr, mg/m2(32228)'],data = Kuparuk_dummy,hue = 'year' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
           borderaxespad=0)
 
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.f'))
plt.gca().invert_yaxis()
ax.set_xticks(range(0,13))
 
 
plt.show()
 

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# Plot: Chlorophyll b periphyton,uncorr  mg/m2 Kuparuk Yearly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Chlorophyll b periphyton,uncorr, mg/m2(32226)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Chlorophyll b periphyton,uncorr, mg/m2(32226)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['year'],y = Kuparuk_dummy['Chlorophyll b periphyton,uncorr, mg/m2(32226)'],data = Kuparuk_dummy,hue = 'month' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
           borderaxespad=0)
 
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.f'))
plt.gca().invert_yaxis()
ax.set_xticks((1974,1975,1976,1977,1978))
 
 
plt.show()
 

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# Plot: Chlorophyll b periphyton,uncorr  mg/m2 Kuparuk Monthly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Chlorophyll b periphyton,uncorr, mg/m2(32226)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Chlorophyll b periphyton,uncorr, mg/m2(32226)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['month'],y = Kuparuk_dummy['Chlorophyll b periphyton,uncorr, mg/m2(32226)'],data = Kuparuk_dummy,hue = 'year' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
           borderaxespad=0)
 
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.f'))
plt.gca().invert_yaxis()
ax.set_xticks(range(0,13))
 
 
plt.show()
 

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# Plot: Phytoplankton, total, cells  /mL(60050)Kuparuk Yearly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Phytoplankton, total, cells  /mL(60050)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Phytoplankton, total, cells  /mL(60050)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['year'],y = Kuparuk_dummy['Phytoplankton, total, cells  /mL(60050)'],data = Kuparuk_dummy,hue = 'month' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.01, 1),
           borderaxespad=0)
 
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.f'))
#plt.gca().invert_yaxis()
#ax.set_xticks((1974,1975,1976,1977,1978))
 
 
plt.show()
 

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# Plot: Phytoplankton, total, cells  /mL(60050)Kuparuk Monthly
 
# Create dummy variable for non NaN Colville
Kuparuk_dummy = Kuparuk[['month','year','Phytoplankton, total, cells  /mL(60050)','Datetime']].copy()
 
# Get rid of all data that is NaN
Kuparuk_dummy = Kuparuk_dummy.replace("", float("NaN"))
Kuparuk_dummy = Kuparuk_dummy.dropna(subset = ['Phytoplankton, total, cells  /mL(60050)'])
Kuparuk_dummy
 
fig, ax = plt.subplots()
sns.scatterplot(x = Kuparuk_dummy['month'],y = Kuparuk_dummy['Phytoplankton, total, cells  /mL(60050)'],data = Kuparuk_dummy,hue = 'year' ,palette='Set1', s =100) 
ax.legend(Kuparuk_dummy['Datetime'],bbox_to_anchor=(1.00, 1),
           borderaxespad=0)
 
#ax.legend(Kuparuk_dummy['month'])
 
ax.yaxis.set_major_formatter(FormatStrFormatter('%.f'))
#plt.gca().invert_yaxis()
#ax.set_xticks((1974,1975,1976,1977,1978))
 
 
plt.show()
 

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Sagavanirtok

↳ 17 cells hidden

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Discharge

Code

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# Plot: Discharge Sagavanirtok from 2020-2021
plt.scatter(Sagavanirtok.loc['2020':'2021']['Datetime'],Sagavanirtok.loc['2020':'2021']['Discharge_ft2/s'],color = 'green', label = 'Sagavanirtok')
plt.legend()
plt.xlabel('Time')
plt.ylabel('Discharge ft2/s')
plt.title('Discharge of the Sagavanirtok River from 2020-2021')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.show()
 

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# Plot: Discharge Sagavanirtok
plt.scatter(Sagavanirtok.loc['2016']['month'],Sagavanirtok.loc['2016']['Discharge_ft2/s'],color = 'red', label = '2016')
plt.scatter(Sagavanirtok.loc['2017']['month'],Sagavanirtok.loc['2017']['Discharge_ft2/s'],color = 'orange', label = '2017')
plt.scatter(Sagavanirtok.loc['2018']['month'],Sagavanirtok.loc['2018']['Discharge_ft2/s'],color = 'yellow', label = '2018')
plt.scatter(Sagavanirtok.loc['2019']['month'],Sagavanirtok.loc['2019']['Discharge_ft2/s'],color = 'green', label = '2019')
plt.scatter(Sagavanirtok.loc['2020']['month'],Sagavanirtok.loc['2020']['Discharge_ft2/s'],color = 'blue', label = '2020')
plt.scatter(Sagavanirtok.loc['2021']['month'],Sagavanirtok.loc['2021']['Discharge_ft2/s'],color = 'purple', label = '2021')
 
plt.legend()
plt.xlabel('Months')
plt.ylabel('Discharge ft2/s')
plt.title('Discharge of the Sagavanirtok River monthly 2016-2021')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.xticks(range(1,13))
 
plt.show()
 

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Gageheight

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# Plot: Gageheight Sagavanirtok from 2020-2021
plt.scatter(Sagavanirtok.loc['2020':'2021']['Datetime'],Sagavanirtok.loc['2020':'2021']['Gageheight_ft'],color = 'green', label = 'Sagavanirtok')
plt.legend()
plt.xlabel('Time')
plt.ylabel('Gageheight (ft)')
plt.title('Gageheight (ft) of the Sagavanirtok River from 2020-2021')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.show()
 

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# Plot: Gageheight Sagavanirtok
plt.scatter(Sagavanirtok.loc['2016']['month'],Sagavanirtok.loc['2016']['Gageheight_ft'],color = 'red', label = '2016')
plt.scatter(Sagavanirtok.loc['2017']['month'],Sagavanirtok.loc['2017']['Gageheight_ft'],color = 'orange', label = '2017')
plt.scatter(Sagavanirtok.loc['2018']['month'],Sagavanirtok.loc['2018']['Gageheight_ft'],color = 'yellow', label = '2018')
plt.scatter(Sagavanirtok.loc['2019']['month'],Sagavanirtok.loc['2019']['Gageheight_ft'],color = 'green', label = '2019')
plt.scatter(Sagavanirtok.loc['2020']['month'],Sagavanirtok.loc['2020']['Gageheight_ft'],color = 'blue', label = '2020')
plt.scatter(Sagavanirtok.loc['2021']['month'],Sagavanirtok.loc['2021']['Gageheight_ft'],color = 'purple', label = '2021')
 
plt.legend()
plt.xlabel('Months')
plt.ylabel('Gageheight (ft)')
plt.title('Gageheight (ft) of the Sagavanirtok River monthly 2016-2021')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.xticks(range(1,13))
plt.show()
 

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Water Temperature

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# Plot: Water Temperature Sagavanirtok 2020-2021
plt.scatter(Sagavanirtok.loc['2020':'2021']['Datetime'],Sagavanirtok.loc['2020':'2021']['Temperature_water_C'],color = 'green', label = 'Sagavanirtok')
plt.legend()
plt.xlabel('Time')
plt.ylabel('Water Temperature (C)')
plt.title('Water Temperature (C) of the Sagavanirtok River from 2020-2021')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.show()

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Code

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# Plot: Water Temperature Sagavanirtok Monthly
plt.scatter(Sagavanirtok['month'],Sagavanirtok['Temperature_water_C'],color = 'green', label = 'Sagavanirtok')
plt.legend()
plt.xlabel('Time')
plt.ylabel('Water Temperature (C)')
plt.title('Water Temperature (C) of the Sagavanirtok River Monthly')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.xticks(range(1,13))
plt.show()

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# Plot: Water Temperature Sagavanirtok Yearly 
plt.scatter(Sagavanirtok.loc['2016']['month'],Sagavanirtok.loc['2016']['Gageheight_ft'],color = 'red', label = '2016')
plt.scatter(Sagavanirtok.loc['2017']['month'],Sagavanirtok.loc['2017']['Gageheight_ft'],color = 'orange', label = '2017')
plt.scatter(Sagavanirtok.loc['2018']['month'],Sagavanirtok.loc['2018']['Gageheight_ft'],color = 'yellow', label = '2018')
plt.scatter(Sagavanirtok.loc['2019']['month'],Sagavanirtok.loc['2019']['Gageheight_ft'],color = 'green', label = '2019')
plt.scatter(Sagavanirtok.loc['2020']['month'],Sagavanirtok.loc['2020']['Gageheight_ft'],color = 'blue', label = '2020')
plt.scatter(Sagavanirtok.loc['2021']['month'],Sagavanirtok.loc['2021']['Gageheight_ft'],color = 'purple', label = '2021')
 
plt.legend()
plt.xlabel('Months')
plt.ylabel('Water Temperature (C)')
plt.title('Water Temperature (C) of the Sagavanirtok River monthly 2016-2021')
plt.rcParams['figure.figsize'] = [8, 8]
plt.xticks(rotation=45)
plt.xticks(range(1,13))
plt.show()
 

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Table of contents

Libraries

1.Colville River

USGS: COLVILLE R AT UMIAT AK

Water Quality Samples

Mean Daily Discharge (ft3/s)

Monthly Average Discharge (ft3/s)

Surface-Water Annual Statistics

Streamflow Measurements

Stream Historical/Current Data (2016)

= All Colville

2.Kuparuk River

USGS: KUPARUK R NR DEADHORSE AK

Water Quality Samples

Mean Daily Discharge (ft3/s)

Monthly Average Discharge (ft3/s)

Surface-Water Annual Statistics

Streamflow Measurements

Monthly Mean Water Temperature (C)

Stream Historical/Current Data (2016)

= All Kuparuk

3.Sagavanirtok River

USGS: SAGAVANIRKTOK R NR PUMP STA 3 AK

Water Quality Samples

Mean Daily Discharge (ft3/s)

Monthly Average Discharge (ft3/s)

Surface-Water Annual Statistics

Streamflow Measurements

Stream Historical/Current Data (2016)

= All Sagavanirtok

PLOTTING

Mapping locations

Plots

All

Colville

Discharge

Precipitation

Air Temperature

Gageheight

Color, Nitrate, Organic carbon

Kuparuk

Discharge

Precipitation

Air Temperature

Gageheight

Color, Nitrate, Organic Carbon

Chlorophyll & Phytoplankton

Sagavanirtok

Discharge

Gageheight

Water Temperature